{ "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_651724/1422729586.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": "554cac36", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['2025100200', '2025100206', '2025100212', '2025100218', '2025100300', '2025100306', '2025100312', '2025100318', '2025100400', '2025100406', '2025100412', '2025100418', '2025100500']\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:12,336 readDiag INFO read_radiance completed in 0.155s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "******************** varName = atms ********************\n", "\n", "########## varType = npp ##########\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_npp_01.2025100200', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_npp_01.2025100206', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_npp_01.2025100212', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_npp_01.2025100218', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_npp_01.2025100300', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_npp_01.2025100306', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_npp_01.2025100312', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_npp_01.2025100318', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_npp_01.2025100400', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_npp_01.2025100406', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_npp_01.2025100412', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_npp_01.2025100418', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_npp_01.2025100500']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_npp_03.2025100200', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_npp_03.2025100206', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_npp_03.2025100212', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_npp_03.2025100218', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_npp_03.2025100300', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_npp_03.2025100306', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_npp_03.2025100312', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_npp_03.2025100318', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_npp_03.2025100400', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_npp_03.2025100406', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_npp_03.2025100412', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_npp_03.2025100418', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_npp_03.2025100500']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 4 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_npp_01.2025100200\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:12,493 readDiag INFO read_radiance completed in 0.152s\n", "2026-10-03 01:55:13,608 readDiag INFO read_radiance completed in 0.135s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_npp_01.2025100206\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:13,875 readDiag INFO read_radiance completed in 0.262s\n", "2026-10-03 01:55:14,847 readDiag INFO read_radiance completed in 0.134s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_npp_01.2025100212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:14,983 readDiag INFO read_radiance completed in 0.132s\n", "2026-10-03 01:55:16,096 readDiag INFO read_radiance completed in 0.141s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_npp_01.2025100218\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:16,240 readDiag INFO read_radiance completed in 0.139s\n", "2026-10-03 01:55:17,241 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_npp_01.2025100300\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:17,376 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:18,388 readDiag INFO read_radiance completed in 0.137s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_npp_01.2025100306\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:18,524 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:19,342 readDiag INFO read_radiance completed in 0.108s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_npp_01.2025100312\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:19,456 readDiag INFO read_radiance completed in 0.109s\n", "2026-10-03 01:55:20,199 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading 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"name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:22,448 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:23,301 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_npp_01.2025100412\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:23,436 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:24,455 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_npp_01.2025100418\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:24,591 readDiag INFO read_radiance completed in 0.132s\n", "2026-10-03 01:55:25,430 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_npp_01.2025100500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:25,566 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Pronto!\n", "\n", "Calculando para a data = 2025100200\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100206\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100212\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100218\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100300\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100306\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100312\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100318\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100400\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100406\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100412\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100418\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100500\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:28,772 readDiag INFO read_radiance completed in 0.133s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "########## varType = n20 ##########\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_n20_01.2025100200', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_n20_01.2025100206', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_n20_01.2025100212', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_n20_01.2025100218', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_n20_01.2025100300', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_n20_01.2025100306', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_n20_01.2025100312', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_n20_01.2025100318', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_n20_01.2025100400', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_n20_01.2025100406', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_n20_01.2025100412', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_n20_01.2025100418', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_n20_01.2025100500']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_n20_03.2025100200', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_n20_03.2025100206', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_n20_03.2025100212', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_n20_03.2025100218', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_n20_03.2025100300', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_n20_03.2025100306', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_n20_03.2025100312', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_n20_03.2025100318', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_n20_03.2025100400', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_n20_03.2025100406', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_n20_03.2025100412', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_n20_03.2025100418', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_n20_03.2025100500']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 4 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_n20_01.2025100200\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:28,906 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:30,022 readDiag INFO read_radiance completed in 0.135s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_n20_01.2025100206\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:30,162 readDiag INFO read_radiance completed in 0.135s\n", "2026-10-03 01:55:31,271 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_n20_01.2025100212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:31,406 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:32,540 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_n20_01.2025100218\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:32,675 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:33,689 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_n20_01.2025100300\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:33,825 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:34,668 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_n20_01.2025100306\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:34,803 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:35,829 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_n20_01.2025100312\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:35,964 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:37,003 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_n20_01.2025100318\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:37,137 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:37,974 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_n20_01.2025100400\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:38,110 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:39,160 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_n20_01.2025100406\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:39,295 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:40,155 readDiag INFO read_radiance completed in 0.150s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_n20_01.2025100412\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:40,326 readDiag INFO read_radiance completed in 0.165s\n", "2026-10-03 01:55:41,437 readDiag INFO read_radiance completed in 0.133s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_n20_01.2025100418\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:41,571 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:42,415 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_n20_01.2025100500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:42,551 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Pronto!\n", "\n", "Calculando para a data = 2025100200\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100206\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100212\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100218\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100300\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100306\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100312\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100318\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100400\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100406\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100412\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100418\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100500\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:45,736 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "########## varType = n21 ##########\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_n21_01.2025100200', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_n21_01.2025100206', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_n21_01.2025100212', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_n21_01.2025100218', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_n21_01.2025100300', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_n21_01.2025100306', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_n21_01.2025100312', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_n21_01.2025100318', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_n21_01.2025100400', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_n21_01.2025100406', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_n21_01.2025100412', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_n21_01.2025100418', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_n21_01.2025100500']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_n21_03.2025100200', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_n21_03.2025100206', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_n21_03.2025100212', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_n21_03.2025100218', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_n21_03.2025100300', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_n21_03.2025100306', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_n21_03.2025100312', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_n21_03.2025100318', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_n21_03.2025100400', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_n21_03.2025100406', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_n21_03.2025100412', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_n21_03.2025100418', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_n21_03.2025100500']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 4 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/diag_atms_n21_01.2025100200\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:45,870 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:46,926 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/diag_atms_n21_01.2025100206\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:47,062 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:48,105 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/diag_atms_n21_01.2025100212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:48,239 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/diag_atms_n21_01.2025100218\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:49,304 readDiag INFO read_radiance completed in 0.245s\n", "2026-10-03 01:55:49,444 readDiag INFO read_radiance completed in 0.136s\n", "2026-10-03 01:55:50,414 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/diag_atms_n21_01.2025100300\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:50,548 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:51,389 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/diag_atms_n21_01.2025100306\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:51,525 readDiag INFO read_radiance completed in 0.131s\n", "2026-10-03 01:55:52,502 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/diag_atms_n21_01.2025100312\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:52,637 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:53,629 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/diag_atms_n21_01.2025100318\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:53,763 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:54,583 readDiag INFO read_radiance completed in 0.130s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/diag_atms_n21_01.2025100400\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:54,717 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:55,724 readDiag INFO read_radiance completed in 0.132s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/diag_atms_n21_01.2025100406\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:55,858 readDiag INFO read_radiance completed in 0.130s\n", "2026-10-03 01:55:56,685 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/diag_atms_n21_01.2025100412\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:56,820 readDiag INFO read_radiance completed in 0.129s\n", "2026-10-03 01:55:57,857 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/diag_atms_n21_01.2025100418\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:57,993 readDiag INFO read_radiance completed in 0.132s\n", "2026-10-03 01:55:58,832 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/diag_atms_n21_01.2025100500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 01:55:58,969 readDiag INFO read_radiance completed in 0.131s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Pronto!\n", "\n", "Calculando para a data = 2025100200\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100206\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100212\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100218\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100300\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100306\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100312\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100318\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100400\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100406\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100412\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100418\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Calculando para a data = 2025100500\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 8)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "SATELITES --> ['npp', 'n20', 'n21']\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# EXP Controle\n", "DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n", "# EXP UMD\n", "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025\"\n", "dateIni = '2025100200' \n", "dateFin = '2025100500'\n", "nHour = '06'\n", "\n", "# varNames = ['amsua','atms']\n", "varNames = ['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", "\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", "maskIqc = f\"(idqc == 0)\"\n", "param = 'omf'\n", "\n", "DF_exp1 = []\n", "Sats = []\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", " #varTypes = ['metop-b']\n", " else:\n", " varTypes = ['npp', 'n20', 'n21']\n", " #varTypes = ['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", " Sats.append(varType)\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", " media_list, desvio_list, nobs_list = [], [], []\n", " tabela = 0\n", " \n", " for date in dates:\n", " print('')\n", " print('Calculando para a data = ', date)\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", " print('')\n", " print('---------> prepara dados para plotagem:')\n", " df_aux1, df_aux = [], []\n", " if mask is None:\n", " print('Todos os canais')\n", " print('mascara idqc => ', maskIqc)\n", " df_aux = df.query(maskIqc)\n", " del(df)\n", " else:\n", " print('mascara canal => ', mask)\n", " df_aux1 = df.query(mask)\n", " print('mascara idqc => ', maskIqc)\n", " print(maskIqc)\n", " df_aux = df_aux1.query(maskIqc)\n", " del(df)\n", "\n", " # Calcular métricas estatísticas básicas\n", " media = np.mean(df_aux[param])\n", " desvio_padrao = np.std(df_aux[param])\n", " n_obs = len(df_aux[param])\n", " \n", " media_list.append(media)\n", " desvio_list.append(desvio_padrao)\n", " nobs_list.append(n_obs)\n", " \n", " tidx += 1\n", "\n", " #print('')\n", " #print('Estatisticas: ')\n", " #print('media = ', media, ' desvio_padrao = ', desvio_padrao, ' n_obs = ', n_obs)\n", " \n", " List_dados = {}\n", " List_dados = {'Data': dates}\n", " \n", " List_dados['media'] = media_list\n", " List_dados['desvio'] = desvio_list\n", " List_dados['nobs'] = nobs_list\n", " \n", " tabela = pd.DataFrame(List_dados).set_index('Data')\n", " \n", " DF_exp1.append(tabela)\n", "\n", " for file in gdf_list:\n", " file.close()\n", "\n", "# ==========================================\n", "# PLOTAGEM DOS DOIS DATAFRAMES NO MESMO GRÁFICO\n", "# ==========================================\n", "tab = 0\n", "ax = None\n", "plt.figure(figsize=(11, 6))\n", "num_sats = len(Sats)\n", "# fig, axes = plt.subplots(num_sats, 1, figsize=(12, num_sats), sharex=True)\n", "\n", "print('SATELITES --> ', Sats)\n", "for sat in Sats:\n", " DF_sat = None\n", " DF_sat = DF_exp1[tab]\n", " #ax = axes[tab]\n", " # 1. Plota o df1 normalmente e guarda o eixo na variável 'ax'\n", " if tab == 0:\n", " ax = DF_sat['media'].plot(kind='line', marker='o', label=sat)\n", " else:\n", " ax = DF_sat['media'].plot(kind='line', marker='o', label=sat, ax=ax)\n", " \n", " tab += 1\n", "\n", "# 2. Plota o df2 usando as mesmas cores, mas com linhas pontilhadas (linestyle='--')\n", "# Passamos ax=ax para que ele desenhe no mesmo gráfico do df1\n", "# DF_final2.plot(kind='line', marker='x', linestyle='--', ax=ax)\n", "\n", "# Captura as cores automáticas que o matplotlib atribuiu às linhas do df1\n", "cores_df1 = [line.get_color() for line in ax.get_lines()]\n", "\n", "# 2. Plota o df2 usando as MESMAS cores coletadas, mas com linha pontilhada\n", "# Passamos a lista de cores no parâmetro 'color'\n", "# DF_final2.plot(kind='line', marker='x', linestyle='--', color=cores_df1, ax=ax)\n", "\n", "# --- CONFIGURAÇÃO DOS TICKS NO FORMATO DDHH (Eixo X) ---\n", "# ax.xaxis.set_major_formatter(mdates.DateFormatter('%d%H')) # Formata para ddhh\n", "# ax.xaxis.set_major_locator(mdates.HourLocator(byhour=[0, 6, 12, 18]))\n", "\n", "#ESCALA LOGARÍTMICA NO EIXO Y\n", "# plt.yscale('log')\n", "\n", "# Customizações do gráfico\n", "plt.title('Média de OmF (idqc = 0)', fontsize=14, fontweight='bold')\n", "plt.xlabel('Data (ddhh)')\n", "plt.ylabel('média(OmF)')\n", "plt.grid(True, linestyle=':', alpha=0.6)\n", "\n", "# Criando as legendas manualmente para diferenciar qual é df1 e df2\n", "# O matplotlib vai combinar as legendas automaticamente, mas podemos ajustar para ficar claro\n", "#plt.legend(title='Satélites (Linha contínua: INPE | Pontilhada: NCEP)', bbox_to_anchor=(0.7, -0.05), loc='upper left', ncol=2)\n", "plt.legend(title='Satélites (Linha contínua: CNT)', bbox_to_anchor=(0.7, -0.05), loc='upper left', ncol=2)\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "da2acc9c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mediadesvionobs
Data
2025100200-0.0005290.16191310549
20251002060.0162060.16761010480
2025100212-0.0221610.16762910415
20251002180.0229670.15745510374
20251003000.0042810.16980710435
20251003060.0082710.17422110523
20251003120.0108000.1887806060
20251003180.0230280.16133110549
20251004000.0053420.16771410570
20251004060.0155840.17674910579
2025100412-0.0084780.16974410591
20251004180.0450400.16362910550
20251005000.0183670.16756310478
\n", "
" ], "text/plain": [ " media desvio nobs\n", "Data \n", "2025100200 -0.000529 0.161913 10549\n", "2025100206 0.016206 0.167610 10480\n", "2025100212 -0.022161 0.167629 10415\n", "2025100218 0.022967 0.157455 10374\n", "2025100300 0.004281 0.169807 10435\n", "2025100306 0.008271 0.174221 10523\n", "2025100312 0.010800 0.188780 6060\n", "2025100318 0.023028 0.161331 10549\n", "2025100400 0.005342 0.167714 10570\n", "2025100406 0.015584 0.176749 10579\n", "2025100412 -0.008478 0.169744 10591\n", "2025100418 0.045040 0.163629 10550\n", "2025100500 0.018367 0.167563 10478" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_exp1[0]" ] }, { "cell_type": "code", "execution_count": 2, "id": "f462bd23", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['2025113000']\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 00:06:07,763 readDiag INFO read_radiance completed in 0.157s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "******************** varName = atms ********************\n", "\n", "########## varType = npp ##########\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_npp_01.2025113000']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_npp_03.2025113000']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_npp_01.2025113000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 00:06:07,914 readDiag INFO read_radiance completed in 0.146s\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 15)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Estatisticas: \n", "media = 0.16029274 desvio_padrao = 1.2327905 n_obs = 10438\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 00:06:09,887 readDiag INFO read_radiance completed in 0.140s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "########## varType = n20 ##########\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_n20_01.2025113000']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_n20_03.2025113000']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_n20_01.2025113000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 00:06:10,025 readDiag INFO read_radiance completed in 0.133s\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 15)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Estatisticas: \n", "media = 0.07136822 desvio_padrao = 1.2167668 n_obs = 10573\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 00:06:11,857 readDiag INFO read_radiance completed in 0.139s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "########## varType = n21 ##########\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_n21_01.2025113000']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_n21_03.2025113000']\n", "\n", "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025/2025113000/diag_atms_n21_01.2025113000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-10-03 00:06:11,995 readDiag INFO read_radiance completed in 0.133s\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", "---------> prepara dados para plotagem:\n", "mascara canal => (nchan == 15)\n", "mascara idqc => (idqc == 0)\n", "(idqc == 0)\n", "\n", "Estatisticas: \n", "media = 0.03950627 desvio_padrao = 1.1760525 n_obs = 10599\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# EXP Controle\n", "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n", "# EXP UMD\n", "DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/ATMS_UMD_warm-OCT-NOV2025\"\n", "dateIni = '2025113000' \n", "dateFin = '2025113000'\n", "nHour = '06'\n", "\n", "# varNames = ['amsua','atms']\n", "varNames = ['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", "\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 = 14\n", "if channel == None:\n", " mask = None\n", "else:\n", " mask = f\"(nchan == {channel})\"\n", "\n", "maskIqc = f\"(idqc == 0)\"\n", "param = 'omf'\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", " #varTypes = ['metop-b']\n", " else:\n", " varTypes = ['npp', 'n20', 'n21']\n", " #varTypes = ['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", " print('')\n", " print('---------> prepara dados para plotagem:')\n", " df_aux1, df_aux = [], []\n", " if mask is None:\n", " print('Todos os canais')\n", " print('mascara idqc => ', maskIqc)\n", " df_aux = df.query(maskIqc)\n", " del(df)\n", " else:\n", " print('mascara canal => ', mask)\n", " df_aux1 = df.query(mask)\n", " print('mascara idqc => ', maskIqc)\n", " print(maskIqc)\n", " df_aux = df_aux1.query(maskIqc)\n", " del(df)\n", " \n", " # Calcular métricas estatísticas básicas\n", " media = np.mean(df_aux[param])\n", " desvio_padrao = np.std(df_aux[param])\n", " n_obs = len(df_aux[param])\n", " \n", " print('')\n", " print('Estatisticas: ')\n", " print('media = ', media, ' desvio_padrao = ', desvio_padrao, ' n_obs = ', n_obs)\n", " \n", " # Plotar o Histograma\n", " plt.figure(figsize=(10, 6))\n", " # bins='auto' ou escolha um número fixo como 50 ou 100 para melhor resolução\n", " counts, bins, patches = plt.hist(df_aux[param], bins=60, density=True, \n", " alpha=0.6, color='salmon', edgecolor='black',\n", " label=f'Resíduos [N={n_obs}]') #'skyblue' -> CNT e 'salmon' -> UMD\n", "\n", " # Adicionar uma curva Gaussiana ideal para comparação\n", " xmin, xmax = plt.xlim()\n", " x = np.linspace(xmin, xmax, 100)\n", " p = (1 / (desvio_padrao * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - media) / desvio_padrao)**2)\n", " plt.plot(x, p, 'r--', linewidth=2, label=f'Ajuste Gaussiano\\n($\\mu$={media:.3f}, $\\sigma$={desvio_padrao:.3f})')\n", "\n", " # Customização do Gráfico\n", " if varName == 'amsua':\n", " plt.title(f'Histograma de Resíduos OmF - {varName}|{varType} (Canal {channel})', loc='left', fontsize=12, \n", " fontweight='bold')\n", " else:\n", " plt.title(f'Histograma de Resíduos OmF - {varName}|{varType} (Canal {channel + 1})', loc='left', fontsize=12, \n", " fontweight='bold')\n", " \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.xlabel('Brightness Temperature (O - B) [K]', fontsize=12)\n", " plt.ylabel('Densidade de Probabilidade', fontsize=12)\n", " plt.grid(True, linestyle=':', alpha=0.6)\n", " plt.legend(loc='upper right', fontsize=10)\n", "\n", " # Salvar e mostrar\n", " if varName == 'amsua':\n", " plt.savefig(f'histograma_{varName}_{varType}_ch{channel}_{datefmt1}.png', dpi=300, bbox_inches='tight')\n", " else:\n", " plt.savefig(f'histograma_{varName}_{varType}_ch{channel+1}_{datefmt1}.png', dpi=300, bbox_inches='tight')\n", " \n", " plt.show()\n", "\n", " for file in gdf_list:\n", " file.close()\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "11365497", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/SSIB/SMNA_CNT_SSIB\"\n", "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/IBIS/CNT_ATMS_warm_OCT-NOV2025\"\n", "# dateIni = '2025101512' \n", "# dateFin = '2025101512'\n", "# nHour = '06'\n", "\n", "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/Inicia_cold/ATMS_AMSUA\"\n", "# DIRdiag = \"/home/radiancia/Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/Inicia_cold/AMSUA\"\n", "# dateIni = '2025102818' \n", "# dateFin = '2025102818'\n", "# nHour = '06'\n", "\n", "DIRdiag = \"/pesq/share/das/dist/luiz.sapucci/SMNA_ATMS/GSIssib_dataout\"\n", "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n", "dateIni = '2025103000' \n", "dateFin = '2025103000'\n", "nHour = '06'\n", "\n", "varNames = ['amsua','atms'] #'amsua' , 'atms'] \n", "# varNames = ['atms'] #'amsua' , 'atms'] \n", "# varNames = ['amsua']\n", "\n", "datei = datetime.strptime(str(dateIni), '%Y%m%d%H')\n", "datef = datetime.strptime(str(dateFin), '%Y%m%d%H')\n", "dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='6h').tolist()]\n", "\n", "print(dates)\n", "print('')\n", "ax = None\n", "\n", "# Pacotes\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import binned_statistic_2d\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", "# Dados para avaliação\n", "channel = 7\n", "if channel == None:\n", " mask = None\n", "else:\n", " mask = f\"(nchan == {channel})\"\n", "\n", "stat = 'mean'\n", "param = 'omf'\n", "\n", "# Definição da malha de retângulos (Resolução da grade)\n", "resolucao = 4.0 # Tamanho do retângulo em graus (ex: 1° x 1°)\n", "\n", "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n", "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n", "\n", "# Configuração correta da figura e do eixo com projeção Cartopy\n", "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", "\n", "# Inicio Loop dos dados de radiancia:\n", "contador = 0\n", "for varName in varNames:\n", " \n", " print('******************** varName = '+varName+' ********************')\n", " print('')\n", " \n", " if varName == 'amsua':\n", " varTypes = ['metop-b', 'metop-c']\n", " else:\n", " varTypes = ['npp', 'n20', 'n21']\n", " if channel == None:\n", " chanATMS = None \n", " mask = None\n", " else:\n", " chanATMS = channel + 1 # vale para canais de temperatura\n", " mask = f\"(nchan == {channel + 1})\" #f\"(nchan == {channel + 1})\" # para canais de temperatura\n", " \n", " for varType in varTypes:\n", " \n", " print('########## varType = '+varType+' ##########')\n", " print('')\n", " \n", " paths, pathsc = [], []\n", "\n", " OuterL = '01' \n", " [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n", "\n", " OuterLc = '03'\n", " [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterLc + '.' + dt) for dt in dates]\n", "\n", " print(paths)\n", " print('')\n", " print(pathsc)\n", "\n", " read = True\n", "\n", " if read: \n", " gdf_list = []\n", " print('')\n", "\n", " print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +\n", " str(int((float(len(paths))*20)/60)) + ' minutos e ' +\n", " str(int((float(len(paths))*20)%60)) + ' segundos.')\n", "\n", " print('')\n", "\n", " for path, pathc in zip(paths, pathsc):\n", " print('Reading ' + path)\n", "\n", " gdf = gd.read_diag(path, pathc)\n", "\n", " gdf_list.append(gdf)\n", "\n", " print('Pronto!')\n", " \n", " tidx = 0\n", " df = []\n", " gdf_list[tidx].pfileinfo()\n", " gdf_list[tidx].obsInfo\n", " df = gdf_list[tidx].obsInfo[varName].loc[varType]\n", " col = df.columns.tolist()\n", " print('columns', col)\n", " \n", " \n", " df_aux = []\n", " if mask is None:\n", " df_aux = df\n", " del(df)\n", " else:\n", " print(mask)\n", " df_aux = df.query(mask)\n", " del(df)\n", " \n", "\n", " # 1. Cálculo do valor stat em cada retângulo\n", " # 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n", " matriz_stat, estatistica = [], []\n", " estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n", " x=df_aux['lon'],\n", " y=df_aux['lat'],\n", " values=df_aux[param],\n", " statistic=stat,\n", " bins=[lon_bins, lat_bins]\n", " )\n", " \n", " # 2. Organização do resultado em formato de matriz\n", " # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n", " matriz_stat = estatistica.T\n", "\n", " # Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n", " matriz_limpa = np.nan_to_num(matriz_stat, nan=0.0)\n", " \n", " print('')\n", " print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n", " print('')\n", " \n", " if contador == 0:\n", " #matriz_final = matriz_stat\n", " matriz_final = matriz_limpa\n", " else:\n", " #matriz_final += matriz_stat\n", " matriz_final += matriz_limpa\n", " \n", " \n", " contador += 1\n", " \n", " for file in gdf_list:\n", " file.close()\n", "\n", "# 3. Fora do loop, calcula a média final se stat for média\n", "print('')\n", "print('contador de satelites =', contador)\n", "if stat == 'mean':\n", " matriz_final = matriz_final / contador\n", " \n", "\n", "# 4. Plotagem da matriz\n", "pcm = ax.pcolormesh(\n", " lon_bins, \n", " lat_bins, \n", " matriz_final, \n", " cmap='RdBu_r', \n", " vmin=-0.50, \n", " vmax=0.50, \n", " transform=ccrs.PlateCarree()\n", ")\n", "\n", "# 5. Adição de elementos geográficos (linhas de costa e estados)\n", "ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n", "ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n", "\n", "# 6. Configuração das linhas de grade e rótulos de Lat/Lon\n", "gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n", "gl.top_labels = False\n", "gl.right_labels = False\n", "\n", "# 7. Barra de cores (Colorbar)\n", "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n", "cbar.set_label(str(stat)+ '( '+str(param)+' ) - cells '+str(resolucao)+'°x'+str(resolucao)+'°')\n", "\n", "if channel is None:\n", " forplot = 'All channels'\n", "else:\n", " forplot = 'Channels = '+str(channel)+' and '+str(channel+1) # AMSUA + ATMS mesmas freq. (apenas canais de temperatura)\n", "instrument_title = 'Radiance' + \" | \" + str(param) + ' - ' + str(varNames)\n", "\n", " \n", "if ax != None:\n", " datefmt1 = dates[0]\n", " datefmt2 = dates[-1]\n", " date1 = datetime.strptime(str(datefmt1), \"%Y%m%d%H\")\n", " date2 = datetime.strptime(str(datefmt2), \"%Y%m%d%H\")\n", " date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%H%M\")) #+ ' GMT'\n", " plt.title(date_title, loc='right', fontsize=10)\n", " plt.title(instrument_title, loc='left', fontsize=9)\n", " ax.annotate(forplot, xy=(0.65, 1.075), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n", " color='gray', fontweight='bold', fontsize='12',horizontalalignment='left', verticalalignment='center')\n", " \n", " figname=\"RadCells_SSIB_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n", " #figname=\"RadCells_AMSUAb_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n", " #figname=\"RadCells_ATMSeAMSUAb_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n", " \n", " plt.tight_layout()\n", " plt.savefig(figname)\n", " \n", "plt.show()\n", "\n", "#matriz_final" ] }, { "cell_type": "code", "execution_count": null, "id": "945ba04d", "metadata": { "scrolled": false }, "outputs": [], "source": [ "\n", "DIRdiag = \"/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n", "dateIni = '2026090118' \n", "dateFin = '2026090118'\n", "nHour = '06'\n", "\n", "varNames = ['amsua','atms'] #'amsua' , 'atms'] \n", "#varNames = ['atms'] #'amsua' , 'atms'] \n", "\n", "datei = datetime.strptime(str(dateIni), '%Y%m%d%H')\n", "datef = datetime.strptime(str(dateFin), '%Y%m%d%H')\n", "dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='6h').tolist()]\n", "\n", "print(dates)\n", "print('')\n", "ax = None\n", "\n", "# Pacotes\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import binned_statistic_2d\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", "# Dados para avaliação\n", "channel = None\n", "if channel == None:\n", " mask = None\n", "else:\n", " mask = f\"(nchan == {channel})\"\n", "\n", "stat = 'count'\n", "param = 'obs'\n", "\n", "# Definição da malha de retângulos (Resolução da grade)\n", "resolucao = 2.0 # Tamanho do retângulo em graus (ex: 1° x 1°)\n", "\n", "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n", "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n", "\n", "# Configuração correta da figura e do eixo com projeção Cartopy\n", "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", "\n", "# Inicio Loop dos dados de radiancia:\n", "contador = 0\n", "for varName in varNames:\n", " \n", " print('******************** varName = '+varName+' ********************')\n", " print('')\n", " \n", " if varName == 'amsua':\n", " varTypes = ['metop-b', 'metop-c']\n", " else:\n", " varTypes = ['npp', 'n20', 'n21']\n", " if channel == None:\n", " chanATMS = None \n", " mask = None\n", " else:\n", " chanATMS = channel + 1 # vale para canais de temperatura\n", " mask = f\"((nchan == {channel + 1}))\" # para canais de temperatura\n", " \n", " for varType in varTypes:\n", " \n", " print('########## varType = '+varType+' ##########')\n", " print('')\n", " \n", " paths, pathsc = [], []\n", "\n", " OuterL = '01' \n", " [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n", "\n", " OuterLc = '03'\n", " [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterLc + '.' + dt) for dt in dates]\n", "\n", " print(paths)\n", " print('')\n", " print(pathsc)\n", "\n", " read = True\n", "\n", " if read: \n", " gdf_list = []\n", " print('')\n", "\n", " print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +\n", " str(int((float(len(paths))*20)/60)) + ' minutos e ' +\n", " str(int((float(len(paths))*20)%60)) + ' segundos.')\n", "\n", " print('')\n", "\n", " for path, pathc in zip(paths, pathsc):\n", " print('Reading ' + path)\n", "\n", " gdf = gd.read_diag(path, pathc)\n", "\n", " gdf_list.append(gdf)\n", "\n", " print('Pronto!')\n", " \n", " tidx = 0\n", " df = []\n", " gdf_list[tidx].pfileinfo()\n", " gdf_list[tidx].obsInfo\n", " df = gdf_list[tidx].obsInfo[varName].loc[varType]\n", " col = df.columns.tolist()\n", " print('columns', col)\n", " \n", " \n", " df_aux = []\n", " if mask is None:\n", " df_aux = df\n", " del(df)\n", " else:\n", " df_aux = df.query(mask)\n", " del(df)\n", " \n", "\n", " # 1. Cálculo do valor stat em cada retângulo\n", " # 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n", " estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n", " x=df_aux['lon'],\n", " y=df_aux['lat'],\n", " values=df_aux[param],\n", " statistic=stat,\n", " bins=[lon_bins, lat_bins]\n", " )\n", " \n", " # 2. Organização do resultado em formato de matriz\n", " # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n", " matriz_stat = estatistica.T\n", "\n", " # Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n", " matriz_limpa = np.nan_to_num(matriz_stat, nan=0.0)\n", " \n", " print('')\n", " print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n", " print('')\n", " \n", " if contador == 0:\n", " matriz_final = matriz_stat\n", " else:\n", " matriz_final += matriz_stat\n", " \n", " \n", " contador += 1\n", " \n", " for file in gdf_list:\n", " file.close()\n", "\n", "# 3. Fora do loop, calcula a média final se stat for média\n", "if stat is 'mean':\n", " matriz_final = matriz_final / contador\n", " \n", "\n", "# 4. Plotagem da matriz\n", "pcm = ax.pcolormesh(\n", " lon_bins, \n", " lat_bins, \n", " matriz_final, \n", " cmap='RdBu_r', \n", " vmin=-200.0, \n", " vmax=200.0, \n", " transform=ccrs.PlateCarree()\n", ")\n", "\n", "# 5. Adição de elementos geográficos (linhas de costa e estados)\n", "ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n", "ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n", "\n", "# 6. Configuração das linhas de grade e rótulos de Lat/Lon\n", "gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n", "gl.top_labels = False\n", "gl.right_labels = False\n", "\n", "# 7. Barra de cores (Colorbar)\n", "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n", "cbar.set_label(str(stat)+ '( '+str(param)+' ) - cells '+str(resolucao)+'°x'+str(resolucao)+'°')\n", "\n", "if channel is None:\n", " forplot = 'All channels'\n", "else:\n", " forplot = 'Channels = '+str(channel)+' and '+str(channel+1) # AMSUA + ATMS mesmas freq. (apenas canais de temperatura)\n", "instrument_title = 'Radiance' + \" | \" + str(param) + ' - ' + str(varNames)\n", "\n", " \n", "if ax != None:\n", " datefmt1 = dates[0]\n", " datefmt2 = dates[-1]\n", " date1 = datetime.strptime(str(datefmt1), \"%Y%m%d%H\")\n", " date2 = datetime.strptime(str(datefmt2), \"%Y%m%d%H\")\n", " date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%H%M\")) #+ ' GMT'\n", " plt.title(date_title, loc='right', fontsize=10)\n", " plt.title(instrument_title, loc='left', fontsize=9)\n", " ax.annotate(forplot, xy=(0.65, 1.075), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n", " color='gray', fontweight='bold', fontsize='12',horizontalalignment='left', verticalalignment='center')\n", " \n", " figname=\"RadCells_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n", " \n", " plt.tight_layout()\n", " plt.savefig(figname)\n", " \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "34e04d80", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6822d4da", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Para uso em máquina local (neste caso, o diretório remoto deverá ser montado localmente com o comando sshfs)\n", "# Exemplo: \n", "# $ sshfs username@egeon.cptec.inpe.br:/pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout\n", "# ou\n", "# $ sshfs username@itapemirim.cptec.inpe.br:/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout\n", "# DIRdiag = '/extra2/EGEON_EXP18_GSI_dataout'\n", "\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepbufr_amsua_INPE/')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepbufr_amsua_NCEP/')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepbufr_conv_NCEP/')\n", "# dateIni = '2025082012' \n", "# dateFin = '2025082012'\n", "# nHour = '6'\n", "\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/IBIS/ATMS_AMSUA/gsi_dataout/SMNA_v3.0.0.t11889-atms-amsu/SMG/datainout/gsi/dataout/')\n", "# dateIni = '2025050906' #'2025082012' \n", "# dateFin = '2025050906' #'2025082012'\n", "# nHour = '6'\n", "\n", "\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/ATMS_AMSUA') # EXP22\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/AMSUA') # EXP22\n", "#DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP22/gsi/dataout/MELISSA/Inicia_cold/ATMS_AMSUA') # EXP22\n", "# dateIni = '2025080112' \n", "# dateFin = '2025080312'\n", "# nHour = '24'\n", "\n", "\n", "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n", "# dateIni = '2025113018' \n", "# dateFin = '2025113018'\n", "# nHour = '06'\n", "\n", "\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_controle') # NCEP\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_GTS-H5')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_DISSM-H5')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_atmsPYT_i95')\n", "# dateIni = '2026041906' \n", "# dateFin = '2026041906'\n", "# nHour = '6'\n", "\n", "DIRdiag = \"/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n", "dateIni = '2026090100' \n", "dateFin = '2026090100'\n", "nHour = '06'\n", "\n", "\n", "#varNames = ['amsua','atms'] #'amsua' , 'atms'] \n", "varNames = ['atms'] #'amsua' , 'atms'] \n", "vminOMA = -1.0 \n", "vmaxOMA = 1.0 \n", "vminSTD = 0.0 \n", "vmaxSTD = 2.0 \n", "\n", "\n", "datei = datetime.strptime(str(dateIni), '%Y%m%d%H')\n", "datef = datetime.strptime(str(dateFin), '%Y%m%d%H')\n", "dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='6h').tolist()]\n", "\n", "print(dates)\n", "print('')\n", "ax = None\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import binned_statistic_2d\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n", "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n", "# mask = f\"((iuse == 1) & (nchan == 3))\" #None\n", "# mask = None\n", "#mask = f\"((nchan == 7))\"\n", "\n", "channel = 7 \n", "mask = f\"(nchan == {channel})\"\n", "stat = 'count'\n", "param = 'obs'\n", "\n", "# 2. Definição da malha de retângulos (Resolução da grade)\n", "resolucao = 2.0 # Tamanho do retângulo em graus (ex: 1° x 1°)\n", "\n", "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n", "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n", "\n", "# 1. Configuração correta da figura e do eixo com projeção Cartopy\n", "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", "\n", "\n", "for varName in varNames:\n", " \n", " print('******************** varName = '+varName+' ********************')\n", " print('')\n", " \n", " if varName == 'amsua':\n", " varTypes = ['metop-b', 'metop-c']\n", " else:\n", " varTypes = ['npp', 'n20', 'n21']\n", " chanATMS = channel + 1 # vale para canais de temperatura\n", " mask = f\"((nchan == {channel + 1}))\" # para canais de temperatura\n", " \n", " #ax = None\n", " for varType in varTypes:\n", " \n", " print('########## varType = '+varType+' ##########')\n", " print('')\n", " \n", " paths, pathsc = [], []\n", "\n", " OuterL = '01' \n", " [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n", "\n", " OuterLc = '03'\n", " [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterLc + '.' + dt) for dt in dates]\n", "\n", " print(paths)\n", " print('')\n", " print(pathsc)\n", "\n", " read = True\n", "\n", " if read: \n", " gdf_list = []\n", " print('')\n", "\n", " print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +\n", " str(int((float(len(paths))*20)/60)) + ' minutos e ' +\n", " str(int((float(len(paths))*20)%60)) + ' segundos.')\n", "\n", " print('')\n", "\n", " for path, pathc in zip(paths, pathsc):\n", " print('Reading ' + path)\n", "\n", " gdf = gd.read_diag(path, pathc)\n", "\n", " gdf_list.append(gdf)\n", "\n", " print('Pronto!')\n", " \n", " tidx = 0\n", " df = []\n", " gdf_list[tidx].pfileinfo()\n", " gdf_list[tidx].obsInfo\n", " #df = gdf_list[tidx].obsInfo\n", " df = gdf_list[tidx].obsInfo[varName].loc[varType]\n", " col = df.columns.tolist()\n", " print('columns', col)\n", " \n", " \n", " df_aux = []\n", " if mask is None:\n", " df_aux = df\n", " del(df)\n", " else:\n", " df_aux = df.query(mask)\n", " del(df)\n", " \n", "\n", " # 3. Cálculo do valor médio de OMA em cada retângulo\n", " # 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n", " estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n", " x=df_aux['lon'],\n", " y=df_aux['lat'],\n", " values=df_aux[param],\n", " statistic=stat,\n", " bins=[lon_bins, lat_bins]\n", " )\n", " \n", " # 4. Organização do resultado em formato de matriz\n", " # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n", " matriz_media = estatistica.T\n", "\n", " # Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n", " matriz_limpa = np.nan_to_num(matriz_media, nan=0.0)\n", " \n", " print('')\n", " print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n", " print('')\n", " \n", " \n", "\n", " # 2. Plotagem da matriz de OMF médio (o restante do código permanece igual)\n", " pcm = ax.pcolormesh(\n", " lon_bins, \n", " lat_bins, \n", " matriz_media, #matriz_oma_media.T,\n", " cmap='RdBu_r', \n", " vmin=-10.0, \n", " vmax=10.0, \n", " transform=ccrs.PlateCarree()\n", " )\n", " \n", " \n", " \n", " for file in gdf_list:\n", " file.close()\n", "\n", "# 3. Adição de elementos geográficos (linhas de costa e estados)\n", "ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n", "ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n", "# ax.add_feature(cfeature.STATES, linestyle='--', linewidth=0.5, edgecolor='gray')\n", "\n", "# 4. Configuração das linhas de grade e rótulos de Lat/Lon\n", "gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n", "gl.top_labels = False\n", "gl.right_labels = False\n", "\n", "# 5. Barra de cores (Colorbar)\n", "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n", "# cbar.set_label('OMF Médio (Valor da Observação - Forecast)')\n", "cbar.set_label(str(stat)+ '( '+str(param)+' ) - cells 2°x2°')\n", "\n", "#plt.title('Distribuição Espacial do OMF Médio por Retângulo', fontsize=14, pad=15)\n", "#plt.show()\n", "\n", "forplot = 'Channels ='+str(channel)+' and '+str(channel+1) # AMSUA + ATMS mesmas freq. (apenas canais de temperatura)\n", "instrument_title = 'Radiance' + \" | \" + str(param)\n", "\n", " \n", "if ax != None:\n", " datefmt1 = dates[0]\n", " datefmt2 = dates[-1]\n", " date1 = datetime.strptime(str(datefmt1), \"%Y%m%d%H\")\n", " date2 = datetime.strptime(str(datefmt2), \"%Y%m%d%H\")\n", " #date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%d%b%Y %H%M\")) #+ ' GMT'\n", " date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%H%M\")) #+ ' GMT'\n", " #ax.annotate(date_title, xy=(0.75, 1.015), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n", " # fontsize=10, horizontalalignment='left', verticalalignment='center')\n", " plt.title(date_title, loc='right', fontsize=10)\n", " #plt.title(instrument_title, loc='left', fontsize=9)\n", " ax.annotate(forplot, xy=(0.65, 1.075), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n", " color='gray', fontweight='bold', fontsize='12',horizontalalignment='left', verticalalignment='center')\n", "\n", " #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"SulAmerica_dates_ALL\"+\"_\"+\"plot.png\"\n", " #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"Melissa_dates_ALL\"+\"_\"+\"plot.png\"\n", " #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word_dates_ALL\"+\"_\"+\"plot.png\"\n", " \n", " figname=\"RadCells_\"+stat+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word\"+\"_\"+str(datefmt1)+\".png\"\n", " \n", " plt.tight_layout()\n", " plt.savefig(figname)\n", " \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "1b14f748", "metadata": {}, "outputs": [], "source": [ "DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_controle')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_atmsPYT_i95')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_GTS-H5')\n", "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_DISSM-H5')\n", "dateIni = '2026041906' \n", "dateFin = '2026041906'\n", "nHour = '6'\n", "\n", "varNames = ['atms'] #['cris-fsr'] #['amsua', 'atms'] #['amsua', 'atms', 'iasi'] \n", "vminOMA = -2.0 \n", "vmaxOMA = 2.0 \n", "vminSTD = 0.0 \n", "vmaxSTD = 14.0 \n", "channel = 6 \n", "\n", "datei = datetime.strptime(str(dateIni), '%Y%m%d%H')\n", "datef = datetime.strptime(str(dateFin), '%Y%m%d%H')\n", "dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='6h').tolist()]\n", "\n", "print(dates)\n", "print('')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import binned_statistic_2d\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n", "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n", "mask = f\"((iuse == 1) & (nchan == 13))\" #None\n", "# mask = None\n", "\n", "\n", "for varName in varNames:\n", " \n", " print('******************** varName = '+varName+' ********************')\n", " print('')\n", " \n", " if varName == 'amsua' or varName == 'iasi':\n", " varTypes = ['metop-b', 'metop-c']\n", " #varTypes = ['metop-b']\n", " else:\n", " #varTypes = ['npp', 'n20', 'n21']\n", " varTypes = ['n20'] #['npp', 'n20', 'n21']\n", " \n", " for varType in varTypes:\n", " \n", " print('########## varType = '+varType+' ##########')\n", " print('')\n", " \n", " paths, pathsc = [], []\n", "\n", " OuterL = '01' \n", " [paths.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterL + '.' + dt) for dt in dates]\n", "\n", " OuterLc = '03'\n", " [pathsc.append(DIRdiag + '/' + dt + '/diag_' + varName +'_'+ varType +'_' + OuterLc + '.' + dt) for dt in dates]\n", "\n", " print(paths)\n", " print('')\n", " print(pathsc)\n", "\n", " read = True\n", "\n", " if read: \n", " gdf_list = []\n", " print('')\n", "\n", " print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +\n", " str(int((float(len(paths))*20)/60)) + ' minutos e ' +\n", " str(int((float(len(paths))*20)%60)) + ' segundos.')\n", "\n", " print('')\n", "\n", " for path, pathc in zip(paths, pathsc):\n", " print('Reading ' + path)\n", "\n", " gdf = gd.read_diag(path, pathc)\n", " \n", " print('Leu!!!!!!!!')\n", "\n", " gdf_list.append(gdf)\n", "\n", " print('Pronto!')\n", " \n", " tidx = 0\n", " df = []\n", " gdf_list[tidx].pfileinfo()\n", " gdf_list[tidx].obsInfo\n", " #df = gdf_list[tidx].obsInfo\n", " df = gdf_list[tidx].obsInfo[varName].loc[varType]\n", " col = df.columns.tolist()\n", " print('columns', col)\n", " \n", " \n", " df_aux = []\n", " if mask is None:\n", " df_aux = df\n", " del(df)\n", " else:\n", " df_aux = df.query(mask)\n", " del(df)\n", " \n", " # 2. Definição da malha de retângulos (Resolução da grade)\n", " resolucao = 5.0 # Tamanho do retângulo em graus (ex: 1° x 1°)\n", "\n", " lon_bins = np.arange(0, 360 + resolucao, resolucao)\n", " lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n", "\n", " # 3. Cálculo do valor médio de OMA em cada retângulo\n", " # 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n", " estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n", " x=df_aux['lon'],\n", " y=df_aux['lat'],\n", " values=df_aux['omf'],\n", " statistic='mean',\n", " bins=[lon_bins, lat_bins]\n", " )\n", " \n", " # 4. Organização do resultado em formato de matriz\n", " # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n", " matriz_media = estatistica.T\n", "\n", " # Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n", " matriz_limpa = np.nan_to_num(matriz_media, nan=0.0)\n", " \n", " print('')\n", " print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n", " print('')\n", " \n", " \n", " # 1. Configuração correta da figura e do eixo com projeção Cartopy\n", " fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", "\n", " # 2. Plotagem da matriz de OMA médio (o restante do código permanece igual)\n", " pcm = ax.pcolormesh(\n", " lon_bins, \n", " lat_bins, \n", " matriz_media, #matriz_oma_media.T,\n", " cmap='RdBu_r', \n", " vmin=-2.0, \n", " vmax=2.0, \n", " transform=ccrs.PlateCarree()\n", " )\n", " \n", " # 3. Adição de elementos geográficos (linhas de costa e estados)\n", " ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n", " ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n", " # ax.add_feature(cfeature.STATES, linestyle='--', linewidth=0.5, edgecolor='gray')\n", "\n", " # 4. Configuração das linhas de grade e rótulos de Lat/Lon\n", " gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n", " gl.top_labels = False\n", " gl.right_labels = False\n", "\n", " # 5. Barra de cores (Colorbar)\n", " cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n", " cbar.set_label('OMA Médio (Valor da Observação - Análise)')\n", "\n", " plt.title('Distribuição Espacial do OMA Médio por Retângulo', fontsize=14, pad=15)\n", " plt.show()\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "0034d27b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import binned_statistic_2d\n", "\n", "# 1. Simulação dos dados de entrada (Substitua pelo carregamento do seu arquivo)\n", "# Exemplo: DataFrame com colunas 'lat', 'lon' e 'oma'\n", "np.random.seed(42)\n", "n_obs = 1000\n", "# dados = pd.DataFrame({\n", "# 'lon': np.random.uniform(-45, -35, n_obs), # Longitude (ex: NE do Brasil)\n", "# 'lat': np.random.uniform(-15, -5, n_obs), # Latitude\n", "# 'oma': np.random.normal(0, 1.5, n_obs) # Valores de OMA\n", "# })\n", "\n", "# Altere a simulação para gerar dados dentro de 0 a 360 (ex: 280 a 320)\n", "dados = pd.DataFrame({\n", " 'lon': np.random.uniform(280, 320, n_obs), # Longitude no padrão 0-360\n", " 'lat': np.random.uniform(-40, 10, n_obs), # Latitude\n", " 'oma': np.random.normal(0, 1.5, n_obs)\n", "})\n", "\n", "# 2. Definição da malha de retângulos (Resolução da grade)\n", "resolucao = 2.0 # Tamanho do retângulo em graus (ex: 1° x 1°)\n", "\n", "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n", "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n", "\n", "# 3. Cálculo do valor médio de OMA em cada retângulo\n", "# 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n", "estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n", " x=dados['lon'],\n", " y=dados['lat'],\n", " values=dados['oma'],\n", " statistic='mean',\n", " bins=[lon_bins, lat_bins]\n", ")\n", "\n", "# 4. Organização do resultado em formato de matriz\n", "# Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n", "matriz_oma_media = estatistica.T\n", "\n", "# Substitui retângulos sem nenhuma observação (NaN) por zero, se necessário\n", "matriz_limpa = np.nan_to_num(matriz_oma_media, nan=0.0)\n", "\n", "print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ff773a08", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", "# 1. Configuração correta da figura e do eixo com projeção Cartopy\n", "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", "\n", "# 2. Plotagem da matriz de OMA médio (o restante do código permanece igual)\n", "pcm = ax.pcolormesh(\n", " lon_bins, \n", " lat_bins, \n", " matriz_oma_media, #matriz_oma_media.T,\n", " cmap='RdBu_r', \n", " vmin=-2.0, \n", " vmax=2.0, \n", " transform=ccrs.PlateCarree()\n", ")\n", "\n", "# 3. Adição de elementos geográficos (linhas de costa e estados)\n", "ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n", "ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n", "# ax.add_feature(cfeature.STATES, linestyle='--', linewidth=0.5, edgecolor='gray')\n", "\n", "# 4. Configuração das linhas de grade e rótulos de Lat/Lon\n", "gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n", "gl.top_labels = False\n", "gl.right_labels = False\n", "\n", "# 5. Barra de cores (Colorbar)\n", "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n", "cbar.set_label('OMA Médio (Valor da Observação - Análise)')\n", "\n", "plt.title('Distribuição Espacial do OMA Médio por Retângulo', fontsize=14, pad=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "655590bb", "metadata": { "tags": [] }, "outputs": [], "source": [ "gdf_list" ] }, { "cell_type": "markdown", "id": "28e3569a-1b5d-451d-9e15-720a9153e6b7", "metadata": {}, "source": [ "Fazendo `tidx = 0`, obtemos o primeiro objeto da lista `gdf_list`:" ] }, { "cell_type": "code", "execution_count": null, "id": "88d8af81", "metadata": { "tags": [] }, "outputs": [], "source": [ "tidx = 0\n", "gdf_list[tidx]" ] }, { "cell_type": "markdown", "id": "f4ad8170-bffd-4c93-a51a-5747b88478b1", "metadata": { "tags": [] }, "source": [ "### Obtendo informações dos arquivos\n", "\n", "Utilize a função `pfileinfo()` para obter uma lista das observações e seus respectivos tipos (`kx`) que estão contidos dentro do arquivo:" ] }, { "cell_type": "code", "execution_count": null, "id": "83af7ed8", "metadata": { "tags": [] }, "outputs": [], "source": [ "gdf_list[tidx].pfileinfo()" ] }, { "cell_type": "code", "execution_count": null, "id": "fb0af990", "metadata": {}, "outputs": [], "source": [ "gdf_list[tidx].obsInfo\n", "df = gdf_list[tidx].obsInfo\n", "col = df.columns.tolist()\n", "print('columns', col)\n", "mask = 'nchan == 6'\n", "df.query(mask).oma" ] }, { "cell_type": "markdown", "id": "baaf3d6a", "metadata": {}, "source": [ "## Utilização da classe `plot_diag`\n", "\n", "Observações: na tabela dos convencionais o `iuse` é `iusev` (mudar no mask)" ] }, { "cell_type": "markdown", "id": "ef2f11f6", "metadata": {}, "source": [ "### Série temporal\n", "\n", "\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 }