{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b8fe8bda",
   "metadata": {},
   "source": [
    "# readDiag - Versão Gerd - Radiância (AMSUA + ATMS)\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_304909/1422729586.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": "8521ca42",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2026082306']\n",
      "\n",
      "******************** varName = atms ********************\n",
      "\n",
      "########## varType = n21 ##########\n",
      "\n",
      "LEITURA EXP 1\n",
      "------------- DIRdiag1 =  /mnt/beegfs/caroline.viezel/ATMS_jaci_FluxoAGT2026\n",
      "\n",
      "['/mnt/beegfs/caroline.viezel/ATMS_jaci_FluxoAGT2026/2026082306/diag_atms_n21_01.2026082306']\n",
      "\n",
      "['/mnt/beegfs/caroline.viezel/ATMS_jaci_FluxoAGT2026/2026082306/diag_atms_n21_03.2026082306']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/ATMS_jaci_FluxoAGT2026/2026082306/diag_atms_n21_01.2026082306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-10-16 15:40:09,945 readDiag INFO read_radiance completed in 0.403s\n",
      "2026-10-16 15:40:10,129 readDiag INFO read_radiance completed in 0.170s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n",
      "\n",
      "LEITURA EXP 2\n",
      "------------- DIRdiag2 =  /mnt/beegfs/caroline.viezel/ATMS_ioper_AGT2026\n",
      "\n",
      "['/mnt/beegfs/caroline.viezel/ATMS_ioper_AGT2026/2026082306/diag_atms_n21_01.2026082306']\n",
      "\n",
      "['/mnt/beegfs/caroline.viezel/ATMS_ioper_AGT2026/2026082306/diag_atms_n21_03.2026082306']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/ATMS_ioper_AGT2026/2026082306/diag_atms_n21_01.2026082306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-10-16 15:40:11,126 readDiag INFO read_radiance completed in 0.220s\n",
      "2026-10-16 15:40:11,323 readDiag INFO read_radiance completed in 0.191s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n",
      "Variable Name : atms\n",
      "              └── kx => n21  \n",
      "\n",
      "columns ['nchan', 'iuse', 'idqc', 'omf', 'emiss', 'oma', 'tb_obs', 'omf_nbc', 'errinv', 'tlach', 'ts', 'pred1', 'pred2', 'pred3', 'pred4', 'pred5', 'pred6', 'pred7', 'pred8', 'pred9', 'pred10', 'pred11', 'pred12', 'pred13', 'pred14', 'spread', 'end_err', 'lat', 'lon', 'time', 'geometry', 'channel', 'obs', 'omf_nobc', 'inverr']\n",
      "\n",
      "Variable Name : atms\n",
      "              └── kx => n21  \n",
      "\n",
      "columns ['nchan', 'iuse', 'idqc', 'omf', 'emiss', 'oma', 'tb_obs', 'omf_nbc', 'errinv', 'tlach', 'ts', 'pred1', 'pred2', 'pred3', 'pred4', 'pred5', 'pred6', 'pred7', 'pred8', 'pred9', 'pred10', 'pred11', 'pred12', 'pred13', 'pred14', 'spread', 'end_err', 'lat', 'lon', 'time', 'geometry', 'channel', 'obs', 'omf_nobc', 'inverr']\n",
      "\n",
      "Channel =  14\n",
      "\n",
      "Total de pontos pareados no canal 14: 6735\n",
      "\n",
      "--- Estatísticas da Diferença (INPE - NCEP) ---\n",
      "Média da diferença nas Observações: 0.0001 K\n",
      "Desvio Padrão da diferença: 0.2700 K\n",
      "Diferença Máxima: 2.9635 K\n",
      "Diferença Mínima: -2.9813 K\n",
      "--- Amostra dos Dados Comparados ---\n",
      "      lat_df1   lon_df1    lat_df2   lon_df2     lat_rd  lon_rd              geometry_df1               geometry_df2     obs_df1     obs_df2  diff_obs\n",
      "0   61.927101   0.48830  61.927151   0.48836  61.930000    0.49    POINT (0.4883 61.9271)   POINT (0.48836 61.92715)  247.812012  247.812027 -0.000015\n",
      "1   63.167400   2.64380  63.167439   2.64383  63.169998    2.64    POINT (2.6438 63.1674)   POINT (2.64383 63.16744)  248.339890  248.339905 -0.000015\n",
      "2   63.776402   3.78590  63.776470   3.78600  63.779999    3.79    POINT (3.7859 63.7764)     POINT (3.786 63.77647)  247.686203  247.686203  0.000000\n",
      "3   64.734901   0.82740  64.734993   0.82748  64.730003    0.83   POINT (0.82739 64.7349)   POINT (0.82748 64.73499)  247.535934  247.535919  0.000015\n",
      "4   64.374603   4.98205  64.374603   4.98205  64.370003    4.98   POINT (4.98206 64.3746)    POINT (4.98206 64.3746)  248.479996  248.404449  0.075546\n",
      "5   64.961098   6.23430  64.961113   6.23431  64.959999    6.23    POINT (6.2343 64.9611)   POINT (6.23431 64.96111)  247.070007  248.713409 -1.643402\n",
      "6   65.358902   1.99800  65.358932   1.99803  65.360001    2.00     POINT (1.998 65.3589)   POINT (1.99803 65.35893)  248.162643  248.162659 -0.000015\n",
      "7   65.971603   3.23000  65.971626   3.23006  65.970001    3.23      POINT (3.23 65.9716)   POINT (3.23006 65.97163)  247.637604  247.637589  0.000015\n",
      "8   65.538002   7.53830  65.538094   7.53838  65.540001    7.54     POINT (7.5383 65.538)   POINT (7.53838 65.53809)  249.880005  248.686935  1.193069\n",
      "9   67.353600   1.93710  67.353607   1.93710  67.349998    1.94    POINT (1.9371 67.3536)    POINT (1.9371 67.35361)  245.410004  246.310104 -0.900101\n",
      "10  67.166801   5.87280  67.166832   5.87288  67.169998    5.87    POINT (5.8728 67.1668)   POINT (5.87288 67.16683)  246.580002  247.301804 -0.721802\n",
      "11  67.749199   7.28600  67.749283   7.28609  67.750000    7.29     POINT (7.286 67.7492)   POINT (7.28609 67.74928)  247.210007  247.221207 -0.011200\n",
      "12  67.195801  11.81040  67.195831  11.81045  67.199997   11.81  POINT (11.81039 67.1958)  POINT (11.81046 67.19583)  248.839996  248.464188  0.375809\n",
      "13  68.608498   0.93350  68.608559   0.93357  68.610001    0.93    POINT (0.9335 68.6085)   POINT (0.93356 68.60856)  245.919724  245.919724  0.000000\n",
      "14  68.573997   4.66350  68.574051   4.66350  68.570000    4.66     POINT (4.6635 68.574)    POINT (4.6635 68.57405)  246.459549  246.459564 -0.000015\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/caroline.viezel/.conda/envs/readDiagGerd/lib/python3.11/site-packages/geopandas/geodataframe.py:1968: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/home/caroline.viezel/.conda/envs/readDiagGerd/lib/python3.11/site-packages/geopandas/geodataframe.py:1968: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/home/caroline.viezel/.conda/envs/readDiagGerd/lib/python3.11/site-packages/geopandas/geodataframe.py:1968: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/home/caroline.viezel/.conda/envs/readDiagGerd/lib/python3.11/site-packages/geopandas/geodataframe.py:1968: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/home/caroline.viezel/.conda/envs/readDiagGerd/lib/python3.11/site-packages/geopandas/geodataframe.py:1968: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/home/caroline.viezel/.conda/envs/readDiagGerd/lib/python3.11/site-packages/geopandas/geodataframe.py:1968: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DIRdiag1 = '/mnt/beegfs/caroline.viezel/ATMS_jaci_FluxoAGT2026'\n",
    "DIRdiag2 = '/mnt/beegfs/caroline.viezel/ATMS_ioper_AGT2026'\n",
    "\n",
    "dateIni = '2026082306' \n",
    "dateFin = '2026082306'\n",
    "nHour = '06'\n",
    "\n",
    "varNames = ['atms']\n",
    "vminOMA = -2.0       \n",
    "vmaxOMA = 2.0        \n",
    "vminSTD = 0.0        \n",
    "vmaxSTD = 14.0       \n",
    "channel = 6 \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",
    "for varName in varNames:\n",
    "    \n",
    "    print('******************** varName = '+varName+' ********************')\n",
    "    print('')\n",
    "    \n",
    "    if varName == 'amsua':\n",
    "        varTypes = ['metop-b', 'metop-c']\n",
    "    else:\n",
    "        varTypes = ['n21']\n",
    "        #varTypes = ['npp', 'n20', 'n21']\n",
    "        \n",
    "    for varType in varTypes:\n",
    "        \n",
    "        print('########## varType = '+varType+' ##########')\n",
    "        print('')\n",
    "        \n",
    "        print('LEITURA EXP 1')\n",
    "        print('------------- DIRdiag1 = ', DIRdiag1)\n",
    "        print('')\n",
    "        \n",
    "        paths, pathsc = [], []\n",
    "\n",
    "        OuterL = '01'        \n",
    "        [paths.append(DIRdiag1 + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "        OuterLc = '03'\n",
    "        [pathsc.append(DIRdiag1 + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + 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_list1 = []\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_list1.append(gdf)\n",
    "\n",
    "            print('Pronto!')\n",
    "            \n",
    "        print('')    \n",
    "        print('LEITURA EXP 2')\n",
    "        print('------------- DIRdiag2 = ', DIRdiag2)\n",
    "        print('')\n",
    "        \n",
    "        paths, pathsc = [], []\n",
    "\n",
    "        OuterL = '01'        \n",
    "        [paths.append(DIRdiag2 + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "        OuterLc = '03'\n",
    "        [pathsc.append(DIRdiag2 + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + 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_list2 = []\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_list2.append(gdf)\n",
    "\n",
    "            print('Pronto!')\n",
    "            \n",
    "        \n",
    "        ##--------------- Tratamento dos dados para fazer a diferença ponto a ponto (lat, lon) correspondente\n",
    "        # EXP 1\n",
    "        tidx = 0\n",
    "        gdf_list1[tidx].pfileinfo()\n",
    "        gdf_list1[tidx].obsInfo\n",
    "        df1 = gdf_list1[tidx].obsInfo\n",
    "        col = df1.columns.tolist()\n",
    "        print('columns', col)\n",
    "        print('')\n",
    "        \n",
    "        \n",
    "        # EXP 2\n",
    "        tidx = 0\n",
    "        gdf_list2[tidx].pfileinfo()\n",
    "        gdf_list2[tidx].obsInfo\n",
    "        df2 = gdf_list2[tidx].obsInfo\n",
    "        col = df2.columns.tolist()\n",
    "        print('columns', col)\n",
    "        print('')\n",
    "        # Opcional: Se houver coluna de pressão ou ID de perfil, inclua no 'on=[]'\n",
    "        \n",
    "        channel = 14\n",
    "        print('Channel = ', channel)\n",
    "        print('')\n",
    "        param = 'obs'\n",
    "        mask = f\"(nchan == {channel})\"\n",
    "        \n",
    "        df1_aux, df2_aux = [], []\n",
    "        \n",
    "        df1_aux = df1.query(mask)\n",
    "        df2_aux = df2.query(mask)\n",
    "        \n",
    "        \n",
    "        # Arredondar coordenadas para o pareamento espacial (ex: 2 casas decimais ~ 1-2 km)\n",
    "        df1_aux['lat_rd'] = df1_aux['lat'].round(2)\n",
    "        df1_aux['lon_rd'] = df1_aux['lon'].round(2)\n",
    "        df1_aux['time_rd'] = df1_aux['time'].round(2)\n",
    "\n",
    "        df2_aux['lat_rd'] = df2_aux['lat'].round(2)\n",
    "        df2_aux['lon_rd'] = df2_aux['lon'].round(2)\n",
    "        df2_aux['time_rd'] = df2_aux['time'].round(2)\n",
    "        \n",
    "        # Fazer o Merge (Cruzamento) usando Lat, Lon e se possível o tempo/ciclo\n",
    "        df_comp = None\n",
    "        df_comp = pd.merge(\n",
    "            df1_aux, df2_aux,\n",
    "            on=['lat_rd', 'lon_rd', 'time_rd'],\n",
    "            suffixes=('_df1', '_df2')\n",
    "        )\n",
    "        \n",
    "        print(f\"Total de pontos pareados no canal {channel}: {len(df_comp)}\")\n",
    "        \n",
    "        # Filtrar apenas os pontos onde AMBOS aceitaram o dado (idqc == 0) \n",
    "        # para comparar a temperatura de brilho sem distorção de rejeições diferentes\n",
    "        df_validos = None\n",
    "        df_validos = df_comp #[(df_comp['idqc_df1'] == 0) & (df_comp['idqc_df2'] == 0)].copy()\n",
    "\n",
    "        # Calcular a diferença (Ex: Grupo menos NCEP, ou Observado vs Observado)\n",
    "        # Nota: A Temperatura de Brilho Observada (Tb) costuma estar na coluna 'tb_obs' ou 'obs' dependendo do formato do diag\n",
    "        df_validos['diff_obs'] = df_validos['obs_df1'] - df_validos['obs_df2']\n",
    "        #df_validos['diff_innov'] = df_validos['innovation_grupo'] - df_validos['innovation_ncep'] # O-B\n",
    "\n",
    "        # Estatísticas Básicas da Diferença\n",
    "        print(\"\\n--- Estatísticas da Diferença (INPE - NCEP) ---\")\n",
    "        print(f\"Média da diferença nas Observações: {df_validos['diff_obs'].mean():.4f} K\")\n",
    "        print(f\"Desvio Padrão da diferença: {df_validos['diff_obs'].std():.4f} K\")\n",
    "        print(f\"Diferença Máxima: {df_validos['diff_obs'].max():.4f} K\")\n",
    "        print(f\"Diferença Mínima: {df_validos['diff_obs'].min():.4f} K\")\n",
    "\n",
    "        # Comparar o comportamento do Controle de Qualidade (idqc)\n",
    "        # Quantos pontos o NCEP aceitou e o Grupo rejeitou, e vice-versa?\n",
    "        #total_ncep_aceitos = len(df_comp[df_comp['idqc_df2'] == 0])\n",
    "        #total_grupo_aceitos = len(df_comp[df_comp['idqc_df1'] == 0])\n",
    "        #print(f\"\\nTotal de pontos aceitos (idqc=0) no NCEP: {total_ncep_aceitos}\")\n",
    "        #print(f\"Total de pontos aceitos (idqc=0) no INPE: {total_grupo_aceitos}\")\n",
    "        #print('')\n",
    "        \n",
    "        # Configura o pandas para mostrar todas as colunas sem cortar no meio\n",
    "        pd.set_option('display.max_columns', None)\n",
    "        pd.set_option('display.width', 1000)\n",
    "\n",
    "        # Seleciona as colunas de interesse (latitude, longitude de ambos e a diferença)\n",
    "        colunas_exibicao = [\n",
    "            'lat_df1', 'lon_df1',               # Coordenadas originais do grupo\n",
    "            'lat_df2', 'lon_df2',               # Coordenadas originais do Ncep\n",
    "            'lat_rd', 'lon_rd',                 # Coordenadas pareadas\n",
    "            'geometry_df1', 'geometry_df2', \n",
    "            #'time_df1', 'time_df2', 'time_rd',  # times\n",
    "            'obs_df1', 'obs_df2',               # Temperaturas de brilho brutas\n",
    "            'diff_obs'                          # A diferença calculada (Grupo - NCEP)\n",
    "        ]\n",
    "\n",
    "        print(\"--- Amostra dos Dados Comparados ---\")\n",
    "        # Mostra as primeiras 15 linhas (ou mude .head() para .sample(15) para ver pontos aleatórios)\n",
    "        print(df_validos[colunas_exibicao].head(15))\n",
    "        print('')\n",
    "        \n",
    "        \n",
    "        import matplotlib.pyplot as plt\n",
    "        import cartopy.crs as ccrs\n",
    "        import cartopy.feature as cfeature\n",
    "\n",
    "        # 1. Cria a figura e o eixo com a projeção Cartopy (PlateCarree é ideal para lat/lon)\n",
    "        fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "        # 2. Adiciona elementos geográficos de referência\n",
    "        ax.coastlines(resolution='110m', color='black', linewidth=0.8)\n",
    "        ax.add_feature(cfeature.BORDERS, linestyle='-', alpha=0.5)\n",
    "        ax.add_feature(cfeature.LAND, facecolor='lightgray', alpha=0.2)\n",
    "        ax.add_feature(cfeature.OCEAN, facecolor='white')\n",
    "\n",
    "        # Adiciona linhas de grade (latitude e longitude)\n",
    "        gl = ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False, alpha=0.5, linestyle='--')\n",
    "        gl.top_labels = False\n",
    "        gl.right_labels = False\n",
    "\n",
    "        # (Opcional) Se quiser dar um zoom na região da Austrália que você estava analisando:\n",
    "        # ax.set_extent([110, 160, -45, -10], crs=ccrs.PlateCarree())\n",
    "\n",
    "        # 3. Plota os pontos pareados usando a diferença como cor\n",
    "        # Usamos 'transform=ccrs.PlateCarree()' porque as colunas lat/lon estão em graus\n",
    "        sc = ax.scatter(\n",
    "            df_validos['lon_rd'], \n",
    "            df_validos['lat_rd'], \n",
    "            c=df_validos['diff_obs'],     # A cor de cada ponto é o valor da diferença\n",
    "            cmap='coolwarm',              # Colormap divergente (azul para negativo, vermelho para positivo)\n",
    "            s=15,                         # Tamanho dos pontos\n",
    "            vmin=-0.5, vmax=0.5,          # Limites da escala de cor (ajuste conforme a magnitude dos seus dados)\n",
    "            transform=ccrs.PlateCarree(),\n",
    "            alpha=0.8\n",
    "        )\n",
    "\n",
    "        # 4. Adiciona a barra de cores (colorbar)\n",
    "        cbar = plt.colorbar(sc, ax=ax, orientation='horizontal', pad=0.08, fraction=0.046)\n",
    "        cbar.set_label('Diferença de Tb: INPE - NCEP [K]', fontsize=11, fontweight='bold')\n",
    "\n",
    "        # 5. Título do Gráfico\n",
    "        plt.title('Comparação '+varName+' Canal 14 ('+varType+'): prepBufr INPE vs NCEP', fontsize=12, fontweight='bold', pad=15)\n",
    "\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "        for file in gdf_list1:\n",
    "            file.close()\n",
    "        for file in gdf_list2:\n",
    "            file.close()\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11365497",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/SSIB/SMNA_CNT_SSIB\"\n",
    "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/IBIS/CNT_ATMS_warm_OCT-NOV2025\"\n",
    "# dateIni = '2025101512' \n",
    "# dateFin = '2025101512'\n",
    "# nHour = '06'\n",
    "\n",
    "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/Inicia_cold/ATMS_AMSUA\"\n",
    "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/Inicia_cold/AMSUA\"\n",
    "# dateIni = '2025102818' \n",
    "# dateFin = '2025102818'\n",
    "# nHour = '06'\n",
    "\n",
    "DIRdiag = \"/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout\"\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n",
    "dateIni = '2025103000' \n",
    "dateFin = '2025103000'\n",
    "nHour = '06'\n",
    "\n",
    "varNames = ['amsua','atms'] #'amsua' , 'atms']  \n",
    "# varNames = ['atms'] #'amsua' , 'atms'] \n",
    "# varNames = ['amsua']\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",
    "ax = None\n",
    "\n",
    "# Pacotes\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",
    "# Dados para avaliação\n",
    "channel = 7\n",
    "if channel == None:\n",
    "    mask = None\n",
    "else:\n",
    "    mask = f\"(nchan == {channel})\"\n",
    "\n",
    "stat = 'mean'\n",
    "param = 'omf'\n",
    "\n",
    "# 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",
    "# 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",
    "# Inicio Loop dos dados de radiancia:\n",
    "contador = 0\n",
    "for varName in varNames:\n",
    "    \n",
    "    print('******************** varName = '+varName+' ********************')\n",
    "    print('')\n",
    "    \n",
    "    if varName == 'amsua':\n",
    "        varTypes = ['metop-b', 'metop-c']\n",
    "    else:\n",
    "        varTypes = ['npp', 'n20', 'n21']\n",
    "        if channel == None:\n",
    "            chanATMS = None \n",
    "            mask = None\n",
    "        else:\n",
    "            chanATMS = channel + 1 # vale para canais de temperatura\n",
    "            mask = f\"(nchan == {channel + 1})\" #f\"(nchan == {channel + 1})\"  # para canais de temperatura\n",
    "            \n",
    "    for varType in varTypes:\n",
    "        \n",
    "        print('########## varType = '+varType+' ##########')\n",
    "        print('')\n",
    "        \n",
    "        paths, pathsc = [], []\n",
    "\n",
    "        OuterL = '01'        \n",
    "        [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "        OuterLc = '03'\n",
    "        [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + 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!')\n",
    "            \n",
    "        tidx = 0\n",
    "        df = []\n",
    "        gdf_list[tidx].pfileinfo()\n",
    "        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",
    "            print(mask)\n",
    "            df_aux = df.query(mask)\n",
    "            del(df)\n",
    "            \n",
    "\n",
    "        # 1. Cálculo do valor stat em cada retângulo\n",
    "        # 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n",
    "        matriz_stat, estatistica = [], []\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=stat,\n",
    "            bins=[lon_bins, lat_bins]\n",
    "        )\n",
    "        \n",
    "        # 2. Organização do resultado em formato de matriz\n",
    "        # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n",
    "        matriz_stat = estatistica.T\n",
    "\n",
    "        # Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n",
    "        matriz_limpa = np.nan_to_num(matriz_stat, nan=0.0)\n",
    "        \n",
    "        print('')\n",
    "        print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n",
    "        print('')\n",
    "        \n",
    "        if contador == 0:\n",
    "            #matriz_final = matriz_stat\n",
    "            matriz_final = matriz_limpa\n",
    "        else:\n",
    "            #matriz_final += matriz_stat\n",
    "            matriz_final += matriz_limpa\n",
    "        \n",
    "        \n",
    "        contador += 1\n",
    "        \n",
    "        for file in gdf_list:\n",
    "            file.close()\n",
    "\n",
    "# 3. Fora do loop, calcula a média final se stat for média\n",
    "print('')\n",
    "print('contador de satelites =', contador)\n",
    "if stat == 'mean':\n",
    "    matriz_final = matriz_final / contador\n",
    "    \n",
    "\n",
    "# 4. Plotagem da matriz\n",
    "pcm = ax.pcolormesh(\n",
    "    lon_bins, \n",
    "    lat_bins, \n",
    "    matriz_final, \n",
    "    cmap='RdBu_r',       \n",
    "    vmin=-0.50,           \n",
    "    vmax=0.50,            \n",
    "    transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "# 5. 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",
    "\n",
    "# 6. 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",
    "# 7. Barra de cores (Colorbar)\n",
    "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "cbar.set_label(str(stat)+ '( '+str(param)+' ) - cells '+str(resolucao)+'°x'+str(resolucao)+'°')\n",
    "\n",
    "if channel is None:\n",
    "    forplot = 'All channels'\n",
    "else:\n",
    "    forplot = 'Channels = '+str(channel)+' and '+str(channel+1)  # AMSUA + ATMS mesmas freq. (apenas canais de temperatura)\n",
    "instrument_title = 'Radiance' + \" | \" + str(param) + ' - ' + str(varNames)\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(\"%H%M\")) #+ ' GMT'\n",
    "    plt.title(date_title, loc='right', fontsize=10)\n",
    "    plt.title(instrument_title, loc='left', fontsize=9)\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=\"RadCells_SSIB_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n",
    "    #figname=\"RadCells_AMSUAb_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n",
    "    #figname=\"RadCells_ATMSeAMSUAb_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(figname)\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "#matriz_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "945ba04d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "DIRdiag = \"/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n",
    "dateIni = '2026090118' \n",
    "dateFin = '2026090118'\n",
    "nHour = '06'\n",
    "\n",
    "varNames = ['amsua','atms'] #'amsua' , 'atms']  \n",
    "#varNames = ['atms'] #'amsua' , 'atms']         \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",
    "ax = None\n",
    "\n",
    "# Pacotes\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",
    "# Dados para avaliação\n",
    "channel = None\n",
    "if channel == None:\n",
    "    mask = None\n",
    "else:\n",
    "    mask = f\"(nchan == {channel})\"\n",
    "\n",
    "stat = 'count'\n",
    "param = 'obs'\n",
    "\n",
    "# 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",
    "# 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",
    "# Inicio Loop dos dados de radiancia:\n",
    "contador = 0\n",
    "for varName in varNames:\n",
    "    \n",
    "    print('******************** varName = '+varName+' ********************')\n",
    "    print('')\n",
    "    \n",
    "    if varName == 'amsua':\n",
    "        varTypes = ['metop-b', 'metop-c']\n",
    "    else:\n",
    "        varTypes = ['npp', 'n20', 'n21']\n",
    "        if channel == None:\n",
    "            chanATMS = None \n",
    "            mask = None\n",
    "        else:\n",
    "            chanATMS = channel + 1 # vale para canais de temperatura\n",
    "            mask = f\"((nchan == {channel + 1}))\"  # para canais de temperatura\n",
    "            \n",
    "    for varType in varTypes:\n",
    "        \n",
    "        print('########## varType = '+varType+' ##########')\n",
    "        print('')\n",
    "        \n",
    "        paths, pathsc = [], []\n",
    "\n",
    "        OuterL = '01'        \n",
    "        [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "        OuterLc = '03'\n",
    "        [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + 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!')\n",
    "            \n",
    "        tidx = 0\n",
    "        df = []\n",
    "        gdf_list[tidx].pfileinfo()\n",
    "        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",
    "        # 1. Cálculo do valor stat 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=stat,\n",
    "            bins=[lon_bins, lat_bins]\n",
    "        )\n",
    "        \n",
    "        # 2. Organização do resultado em formato de matriz\n",
    "        # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n",
    "        matriz_stat = estatistica.T\n",
    "\n",
    "        # Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n",
    "        matriz_limpa = np.nan_to_num(matriz_stat, nan=0.0)\n",
    "        \n",
    "        print('')\n",
    "        print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n",
    "        print('')\n",
    "        \n",
    "        if contador == 0:\n",
    "            matriz_final = matriz_stat\n",
    "        else:\n",
    "            matriz_final += matriz_stat\n",
    "        \n",
    "        \n",
    "        contador += 1\n",
    "        \n",
    "        for file in gdf_list:\n",
    "            file.close()\n",
    "\n",
    "# 3. Fora do loop, calcula a média final se stat for média\n",
    "if stat is 'mean':\n",
    "    matriz_final = matriz_final / contador\n",
    "    \n",
    "\n",
    "# 4. Plotagem da matriz\n",
    "pcm = ax.pcolormesh(\n",
    "    lon_bins, \n",
    "    lat_bins, \n",
    "    matriz_final, \n",
    "    cmap='RdBu_r',       \n",
    "    vmin=-200.0,           \n",
    "    vmax=200.0,            \n",
    "    transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "# 5. 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",
    "\n",
    "# 6. 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",
    "# 7. Barra de cores (Colorbar)\n",
    "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "cbar.set_label(str(stat)+ '( '+str(param)+' ) - cells '+str(resolucao)+'°x'+str(resolucao)+'°')\n",
    "\n",
    "if channel is None:\n",
    "    forplot = 'All channels'\n",
    "else:\n",
    "    forplot = 'Channels = '+str(channel)+' and '+str(channel+1)  # AMSUA + ATMS mesmas freq. (apenas canais de temperatura)\n",
    "instrument_title = 'Radiance' + \" | \" + str(param) + ' - ' + str(varNames)\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(\"%H%M\")) #+ ' GMT'\n",
    "    plt.title(date_title, loc='right', fontsize=10)\n",
    "    plt.title(instrument_title, loc='left', fontsize=9)\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=\"RadCells_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(figname)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34e04d80",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6822d4da",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "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",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/ATMS_AMSUA') # EXP22\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/AMSUA') # EXP22\n",
    "#DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/Inicia_cold/ATMS_AMSUA') # EXP22\n",
    "# dateIni = '2025080112' \n",
    "# dateFin = '2025080312'\n",
    "# nHour = '24'\n",
    "\n",
    "\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n",
    "# dateIni = '2025113018' \n",
    "# dateFin = '2025113018'\n",
    "# nHour = '06'\n",
    "\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_controle') # NCEP\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_GTS-H5')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_DISSM-H5')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_atmsPYT_i95')\n",
    "# dateIni = '2026041906' \n",
    "# dateFin = '2026041906'\n",
    "# nHour = '6'\n",
    "\n",
    "DIRdiag = \"/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n",
    "dateIni = '2026090100' \n",
    "dateFin = '2026090100'\n",
    "nHour = '06'\n",
    "\n",
    "\n",
    "#varNames = ['amsua','atms'] #'amsua' , 'atms']  \n",
    "varNames = ['atms'] #'amsua' , 'atms']  \n",
    "vminOMA = -1.0       \n",
    "vmaxOMA = 1.0        \n",
    "vminSTD = 0.0        \n",
    "vmaxSTD = 2.0       \n",
    "\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",
    "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 == 7))\"\n",
    "\n",
    "channel = 7 \n",
    "mask = f\"(nchan == {channel})\"\n",
    "stat = 'count'\n",
    "param = 'obs'\n",
    "\n",
    "# 2. 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",
    "# 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",
    "\n",
    "for varName in varNames:\n",
    "    \n",
    "    print('******************** varName = '+varName+' ********************')\n",
    "    print('')\n",
    "    \n",
    "    if varName == 'amsua':\n",
    "        varTypes = ['metop-b', 'metop-c']\n",
    "    else:\n",
    "        varTypes = ['npp', 'n20', 'n21']\n",
    "        chanATMS = channel + 1 # vale para canais de temperatura\n",
    "        mask = f\"((nchan == {channel + 1}))\"  # para canais de temperatura\n",
    "        \n",
    "    #ax = None\n",
    "    for varType in varTypes:\n",
    "        \n",
    "        print('########## varType = '+varType+' ##########')\n",
    "        print('')\n",
    "        \n",
    "        paths, pathsc = [], []\n",
    "\n",
    "        OuterL = '01'        \n",
    "        [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "        OuterLc = '03'\n",
    "        [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + 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!')\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 OMA 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=stat,\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, se necessário\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",
    "        # 2. Plotagem da matriz de OMF médio (o restante do código permanece igual)\n",
    "        pcm = ax.pcolormesh(\n",
    "            lon_bins, \n",
    "            lat_bins, \n",
    "            matriz_media,  #matriz_oma_media.T,\n",
    "            cmap='RdBu_r',       \n",
    "            vmin=-10.0,           \n",
    "            vmax=10.0,            \n",
    "            transform=ccrs.PlateCarree()\n",
    "        )\n",
    "            \n",
    "        \n",
    "        \n",
    "        for file in gdf_list:\n",
    "            file.close()\n",
    "\n",
    "# 3. 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",
    "# 4. 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",
    "# 5. 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(str(stat)+ '( '+str(param)+' ) - cells 2°x2°')\n",
    "\n",
    "#plt.title('Distribuição Espacial do OMF Médio por Retângulo', fontsize=14, pad=15)\n",
    "#plt.show()\n",
    "\n",
    "forplot = 'Channels ='+str(channel)+' and '+str(channel+1)  # AMSUA + ATMS mesmas freq. (apenas canais de temperatura)\n",
    "instrument_title = 'Radiance' + \" | \" + 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=9)\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",
    "    \n",
    "    figname=\"RadCells_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(figname)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b14f748",
   "metadata": {},
   "outputs": [],
   "source": [
    "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_atmsPYT_i95')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_GTS-H5')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_DISSM-H5')\n",
    "dateIni = '2026041906' \n",
    "dateFin = '2026041906'\n",
    "nHour = '6'\n",
    "\n",
    "varNames = ['atms'] #['cris-fsr'] #['amsua', 'atms'] #['amsua', 'atms', 'iasi']       \n",
    "vminOMA = -2.0       \n",
    "vmaxOMA = 2.0        \n",
    "vminSTD = 0.0        \n",
    "vmaxSTD = 14.0       \n",
    "channel = 6 \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",
    "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 == 13))\" #None\n",
    "# mask = None\n",
    "\n",
    "\n",
    "for varName in varNames:\n",
    "    \n",
    "    print('******************** varName = '+varName+' ********************')\n",
    "    print('')\n",
    "    \n",
    "    if varName == 'amsua' or varName == 'iasi':\n",
    "        varTypes = ['metop-b', 'metop-c']\n",
    "        #varTypes = ['metop-b']\n",
    "    else:\n",
    "        #varTypes = ['npp', 'n20', 'n21']\n",
    "        varTypes = ['n20'] #['npp', 'n20', 'n21']\n",
    "        \n",
    "    for varType in varTypes:\n",
    "        \n",
    "        print('########## varType = '+varType+' ##########')\n",
    "        print('')\n",
    "        \n",
    "        paths, pathsc = [], []\n",
    "\n",
    "        OuterL = '01'        \n",
    "        [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "        OuterLc = '03'\n",
    "        [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + 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",
    "                print('Leu!!!!!!!!')\n",
    "\n",
    "                gdf_list.append(gdf)\n",
    "\n",
    "            print('Pronto!')\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",
    "        # 2. Definição da malha de retângulos (Resolução da grade)\n",
    "        resolucao = 5.0  # 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",
    "        # 3. Cálculo do valor médio de OMA 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['omf'],\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, se necessário\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",
    "        # 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",
    "        # 2. Plotagem da matriz de OMA médio (o restante do código permanece igual)\n",
    "        pcm = ax.pcolormesh(\n",
    "            lon_bins, \n",
    "            lat_bins, \n",
    "            matriz_media,  #matriz_oma_media.T,\n",
    "            cmap='RdBu_r',       \n",
    "            vmin=-2.0,           \n",
    "            vmax=2.0,            \n",
    "            transform=ccrs.PlateCarree()\n",
    "        )\n",
    " \n",
    "        # 3. 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",
    "        # 4. 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",
    "        # 5. Barra de cores (Colorbar)\n",
    "        cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "        cbar.set_label('OMA Médio (Valor da Observação - Análise)')\n",
    "\n",
    "        plt.title('Distribuição Espacial do OMA Médio por Retângulo', fontsize=14, pad=15)\n",
    "        plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0034d27b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binned_statistic_2d\n",
    "\n",
    "# 1. Simulação dos dados de entrada (Substitua pelo carregamento do seu arquivo)\n",
    "# Exemplo: DataFrame com colunas 'lat', 'lon' e 'oma'\n",
    "np.random.seed(42)\n",
    "n_obs = 1000\n",
    "# dados = pd.DataFrame({\n",
    "#     'lon': np.random.uniform(-45, -35, n_obs),  # Longitude (ex: NE do Brasil)\n",
    "#     'lat': np.random.uniform(-15, -5, n_obs),   # Latitude\n",
    "#     'oma': np.random.normal(0, 1.5, n_obs)       # Valores de OMA\n",
    "# })\n",
    "\n",
    "# Altere a simulação para gerar dados dentro de 0 a 360 (ex: 280 a 320)\n",
    "dados = pd.DataFrame({\n",
    "    'lon': np.random.uniform(280, 320, n_obs),  # Longitude no padrão 0-360\n",
    "    'lat': np.random.uniform(-40, 10, n_obs),   # Latitude\n",
    "    'oma': np.random.normal(0, 1.5, n_obs)\n",
    "})\n",
    "\n",
    "# 2. 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",
    "# 3. Cálculo do valor médio de OMA 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=dados['lon'],\n",
    "    y=dados['lat'],\n",
    "    values=dados['oma'],\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_oma_media = estatistica.T\n",
    "\n",
    "# Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n",
    "matriz_limpa = np.nan_to_num(matriz_oma_media, nan=0.0)\n",
    "\n",
    "print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff773a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\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",
    "# 2. Plotagem da matriz de OMA médio (o restante do código permanece igual)\n",
    "pcm = ax.pcolormesh(\n",
    "    lon_bins, \n",
    "    lat_bins, \n",
    "    matriz_oma_media,  #matriz_oma_media.T,\n",
    "    cmap='RdBu_r',       \n",
    "    vmin=-2.0,           \n",
    "    vmax=2.0,            \n",
    "    transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "# 3. 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",
    "# 4. 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",
    "# 5. Barra de cores (Colorbar)\n",
    "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "cbar.set_label('OMA Médio (Valor da Observação - Análise)')\n",
    "\n",
    "plt.title('Distribuição Espacial do OMA Médio por Retângulo', fontsize=14, pad=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "655590bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "id": "88d8af81",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "id": "83af7ed8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdf_list[tidx].pfileinfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb0af990",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "mask = 'nchan == 6'\n",
    "df.query(mask).oma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baaf3d6a",
   "metadata": {},
   "source": [
    "## Utilização da classe `plot_diag`\n",
    "\n",
    "Observações: na tabela dos convencionais o `iuse` é `iusev` (mudar no mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2f11f6",
   "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_radi()` (nova função), incluída na classe `plot_diag`. Inicialmente é gerada uma figura com os parâmetros já fixados nesta seção. Em seguida são apresentadas figuras alterando o parâmetro `channel`.\n",
    "\n",
    "Plotando uma série temporal do OmA e OmF para os dados do canal setado na célula de definição das variáveis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "363f269f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# channel = None\n",
    "channel = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
    "\n",
    "gd.plot_diag.time_series_radi(gdf_list,\n",
    "                             varName=varName, \n",
    "                             varType=varType, \n",
    "                             mask='idqc==0 & iuse>=1', \n",
    "                             dateIni=dateIni, \n",
    "                             dateFin=dateFin, \n",
    "                             nHour=nHour, \n",
    "                             vminOMA=vminOMA, \n",
    "                             vmaxOMA=vmaxOMA, \n",
    "                             vminSTD=vminSTD, \n",
    "                             vmaxSTD=vmaxSTD, \n",
    "                             channel=channel, \n",
    "                             Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04f193a",
   "metadata": {},
   "source": [
    "### Distribuição espacial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6297ca3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "varNames = ['ps','t','q','uv','gps']\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\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('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c16f7d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "param = 'iuse'\n",
    "tidx = 0\n",
    "\n",
    "gd.plot_diag.plot(gdf_list[tidx], \n",
    "                  varName=varName, \n",
    "                  varType=varType, \n",
    "                  param=param, \n",
    "                  mask='iuse >= -1', \n",
    "                  legend='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82df541",
   "metadata": {},
   "source": [
    "Para salvar a figura, defina o seu nome (`figname`) e execute os comandos a seguir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84169c19",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "figname = varName + '_' + param + '_' + 'plot.png'\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a240e9d-3d2a-48e3-a76a-f09fc6331a1d",
   "metadata": {},
   "source": [
    "Gerando a mesma figura, mas considerando vários tipos diferentes (`kx`) da observação selecionada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0be7a398",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# varTypes = [254,242,221,220,257,258,281,280]\n",
    "##### PrepBufr - INPE\n",
    "# # varTypes = [244,  260,  255,  281,  253,  243,  254,  220,  252,  242,  250,  245,  246,  240,  247]\n",
    "# varTypes = [244,  260,  255,  281,  220,  253,  243,  254,  280,  230,  252,  242,  250,  245,  247,  246,  240]\n",
    "# varName = 'uv'\n",
    "# # varTypes = [269,  267,  42,  3,  66,  755,  753,  5,  751,  44,  43,  825]\n",
    "# varTypes = [267,  269,  3,  5,  66,  753,  751,  754,  750,  755,  44,  42,  43,  825]\n",
    "# varName = 'gps'\n",
    "# # varTypes = [120]\n",
    "# varTypes = []\n",
    "# varName = 'q'\n",
    "# # varTypes = [181,  120]\n",
    "# varTypes = [181,  120,  180,  130]\n",
    "# varName = 't'\n",
    "# # varTypes = [181,  120]\n",
    "varTypes = [181,  180]\n",
    "varName = 'ps'\n",
    "#### PrepBufr - NCEP\n",
    "# # varTypes = [244,  260,  255,  281,  253,  243,  254,  220,  287,  280,  284,  221,  230,  252,  242,\n",
    "# #             250,  210,  245,  246,  240,  247,  224,  232]\n",
    "# varTypes = [244,  260,  255,  281,  253,  243,  254,  287,  280,  284,  221,  220,  252,  242,  250,\n",
    "#             210,  245,  247,  246,  240,  230,  224,  232]\n",
    "# varName = 'uv'\n",
    "# # varTypes = [269,  267,  42,  3,  66,  755,  753,  5,  751,  44,  43,  825]\n",
    "# varTypes = [267,  269,  3,  5,  66,  753,  751,  754,  750,  755,  44,  42,  43,  825]\n",
    "# varName = 'gps'\n",
    "# # varTypes = [181,  120,  187,  180,  183,  132,  182]\n",
    "# varTypes = [181,  187,  180,  183,  120,  132]\n",
    "# varName = 'q'\n",
    "# # varTypes = [181,  120,  187,  180,  183,  130,  126,  132,  182]\n",
    "# varTypes = [181,  187,  180,  183,  120,  130,  132,  126]\n",
    "# varName = 't'\n",
    "# # varTypes = [181,  180,  120,  187,  183,  132,  182]\n",
    "# varTypes = [181,  180,  187,  183,  120]\n",
    "# varName = 'ps'\n",
    "idschar = '_'.join([str(item) for item in varTypes])\n",
    "\n",
    "gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)\n",
    "\n",
    "figname = varName + '_' + idschar + '_' + 'ptmap.png'\n",
    "\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 = ['t'] #['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==1')\n",
    "\n",
    "figname = idschar + '_pvmap.png'\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "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 = 't'\n",
    "gd.plot_diag.pcount(gdf_list[tidx], varName)"
   ]
  },
  {
   "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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType, \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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "SingleL = 'OneL'\n",
    "Lay = 15\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType, \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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Level = 'Zlevs'\n",
    "Lay = 15\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType, \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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Level = 'Zlevs'\n",
    "Lay = None\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType, \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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Level = None\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType, \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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