{ "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_1493289/1422729586.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": 6, "id": "945ba04d", "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:152: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", "<>:152: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['2026090118']\n", "\n", "******************** varName = amsua ********************\n", "\n", "########## varType = metop-b ##########\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_amsua_metop-b_01.2026090118']\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_amsua_metop-b_03.2026090118']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_amsua_metop-b_01.2026090118\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-09-24 14:03:41,032 readDiag INFO read_radiance completed in 0.263s\n", "2026-09-24 14:03:41,248 readDiag INFO read_radiance completed in 0.210s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Pronto!\n", "Variable Name : amsua\n", " └── kx => metop-b \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", "Formato da matriz gerada (Latitudes, Longitudes): (90, 180)\n", "\n", "########## varType = metop-c ##########\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_amsua_metop-c_01.2026090118']\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_amsua_metop-c_03.2026090118']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_amsua_metop-c_01.2026090118\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-09-24 14:03:42,507 readDiag INFO read_radiance completed in 0.171s\n", "2026-09-24 14:03:42,715 readDiag INFO read_radiance completed in 0.185s\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", "\n", "Formato da matriz gerada (Latitudes, Longitudes): (90, 180)\n", "\n", "******************** varName = atms ********************\n", "\n", "########## varType = npp ##########\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_npp_01.2026090118']\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_npp_03.2026090118']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_npp_01.2026090118\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-09-24 14:03:43,259 readDiag INFO read_radiance completed in 0.261s\n", "2026-09-24 14:03:43,577 readDiag INFO read_radiance completed in 0.293s\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", "\n", "Formato da matriz gerada (Latitudes, Longitudes): (90, 180)\n", "\n", "########## varType = n20 ##########\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_n20_01.2026090118']\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_n20_03.2026090118']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_n20_01.2026090118\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-09-24 14:03:45,517 readDiag INFO read_radiance completed in 0.269s\n", "2026-09-24 14:03:45,827 readDiag INFO read_radiance completed in 0.281s\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", "\n", "Formato da matriz gerada (Latitudes, Longitudes): (90, 180)\n", "\n", "########## varType = n21 ##########\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_n21_01.2026090118']\n", "\n", "['/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_n21_03.2026090118']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026090118/diag_atms_n21_01.2026090118\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-09-24 14:03:47,244 readDiag INFO read_radiance completed in 0.187s\n", "2026-09-24 14:03:47,454 readDiag INFO read_radiance completed in 0.185s\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", "Formato da matriz gerada (Latitudes, Longitudes): (90, 180)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_1493289/1749648888.py:152: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " if stat is 'mean':\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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", "\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", "\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 }