{
 "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_1867104/1422729586.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": "11365497",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2025103000']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:27,583 readDiag INFO read_radiance completed in 0.101s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************** varName = amsua ********************\n",
      "\n",
      "########## varType = metop-b ##########\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_amsua_metop-b_01.2025103000']\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_amsua_metop-b_03.2025103000']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_amsua_metop-b_01.2025103000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:27,680 readDiag INFO read_radiance completed in 0.094s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n",
      "Variable Name : amsua\n",
      "              └── kx => metop-b  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:28,275 readDiag INFO read_radiance completed in 0.075s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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",
      "(nchan == 7)\n",
      "\n",
      "Formato da matriz gerada (Latitudes, Longitudes): (45, 90)\n",
      "\n",
      "########## varType = metop-c ##########\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_amsua_metop-c_01.2025103000']\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_amsua_metop-c_03.2025103000']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_amsua_metop-c_01.2025103000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:28,378 readDiag INFO read_radiance completed in 0.077s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n",
      "Variable Name : amsua\n",
      "              └── kx => metop-c  \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",
      "(nchan == 7)\n",
      "\n",
      "Formato da matriz gerada (Latitudes, Longitudes): (45, 90)\n",
      "\n",
      "******************** varName = atms ********************\n",
      "\n",
      "########## varType = npp ##########\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_npp_01.2025103000']\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_npp_03.2025103000']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_npp_01.2025103000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:28,938 readDiag INFO read_radiance completed in 0.136s\n",
      "2026-09-29 15:42:29,120 readDiag INFO read_radiance completed in 0.144s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n",
      "Variable Name : atms\n",
      "              └── kx => npp  \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",
      "(nchan == 8)\n",
      "\n",
      "Formato da matriz gerada (Latitudes, Longitudes): (45, 90)\n",
      "\n",
      "########## varType = n20 ##########\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_n20_01.2025103000']\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_n20_03.2025103000']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_n20_01.2025103000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:30,266 readDiag INFO read_radiance completed in 0.130s\n",
      "2026-09-29 15:42:30,406 readDiag INFO read_radiance completed in 0.131s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n",
      "Variable Name : atms\n",
      "              └── kx => n20  \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",
      "(nchan == 8)\n",
      "\n",
      "Formato da matriz gerada (Latitudes, Longitudes): (45, 90)\n",
      "\n",
      "########## varType = n21 ##########\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_n21_01.2025103000']\n",
      "\n",
      "['/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_n21_03.2025103000']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout/2025103000/diag_atms_n21_01.2025103000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-09-29 15:42:31,566 readDiag INFO read_radiance completed in 0.133s\n",
      "2026-09-29 15:42:31,698 readDiag INFO read_radiance completed in 0.130s\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",
      "(nchan == 8)\n",
      "\n",
      "Formato da matriz gerada (Latitudes, Longitudes): (45, 90)\n",
      "\n",
      "\n",
      "contador de satelites = 5\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "readDiagGerd",
   "language": "python",
   "name": "readdiaggerd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}