06_introduccion_nco.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPuwj8avbrY71dW/nalrAqC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/waveology/kitchen/blob/main/06_introduccion_nco.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "<html><img src=\"https://owncloud.aemet.es/index.php/s/lwiFbCGBRAJHlF7/download?path=%2F&files=logo.png\"></html>\n",
        "\n",
        "\n",
        "* **Autor:** Ernesto Barrera\n",
        "* **Institución:** AEMET\n",
        "---"
      ],
      "metadata": {
        "id": "RZM-kw5U9pdX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Introducción a NCO (netCDF Operators)"
      ],
      "metadata": {
        "id": "tkK3IG23YR1G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "cRX7RMJSZVVW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* [Web del proyecto](https://nco.sourceforge.net/)\n",
        "* Guía de usuario:  [PDF](https://nco.sourceforge.net/nco.pdf)   /   [HTML](https://nco.sourceforge.net/nco.html)\n",
        "* [Código fuente](https://nco.sourceforge.net/#Source)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_wFDVUQUZl-a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **¿Qué es NCO?** Un pequeño conjunto de operadores que actúan sobre ficheros en formato NetCDF.\n",
        "\n",
        "* **¿Cómo se usa NCO?** Los operadores se invocan en línea de comandos con diversos modificadores.\n",
        "\n",
        "* **¿Cuándo usar NCO?** Cuando se requiera el preproceso eficiente en memoria de datos en formato NetCDF.\n",
        "\n",
        "* **¿Por qué NCO?** Porque simplifica la operativa al realizar multitud de operaciones con unos pocos operadores."
      ],
      "metadata": {
        "id": "ULQXd_YDb5w-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Operadores"
      ],
      "metadata": {
        "id": "KaKh4HpgwLYn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Funciones de los operadores\n",
        "\n",
        "* Aritmética: **ncap2**\n",
        "* Editor de atributos: **ncatted**\n",
        "* Operador binario: **ncbo**\n",
        "* Generador de climatologías: **ncclimo**\n",
        "* Estadística de ensembles: **nces**\n",
        "* Concatenador de ensembles: **ncecat**\n",
        "* Interpolación: **ncflint**\n",
        "* Cajón de sastre (kitchen sink): **ncks**\n",
        "* Permuta y empaquetado: **ncpdq**\n",
        "* Promedio de registros: **ncra**\n",
        "* Concatenador de registros: **ncrcat**\n",
        "* Remapeador: **ncremap**\n",
        "* Renombrador: **ncrename**\n",
        "* Promedios ponderados: **ncwa**"
      ],
      "metadata": {
        "id": "67CrA8tfdzNL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Instalación"
      ],
      "metadata": {
        "id": "QDE0icrRcLoc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Hay más de una forma de instalar NCO\n",
        "* Para este noetbook usaremos el repositorio Conda de Colab\n"
      ],
      "metadata": {
        "id": "uD0bttWjcQ3Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instala conda en Colab\n",
        "# Este proceso implica un reinicio del kernel y\n",
        "# es normal que aparezca un mensaje de fallo en la sesión\n",
        "# --------------------------------------------------------\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "metadata": {
        "id": "W9KyqC0d5asu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verifica la instalación\n",
        "# ------------------------\n",
        "import condacolab\n",
        "condacolab.check()"
      ],
      "metadata": {
        "id": "eALhXEKhUSXm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instala NCO\n",
        "# -----------------\n",
        "!mamba install -q nco"
      ],
      "metadata": {
        "id": "gJ-sykx_6zWw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Especificación de la ruta de udunits (Colab)\n",
        "# ---------------------------------------------\n",
        "import os\n",
        "os.environ['UDUNITS2_XML_PATH'] = '/usr/local/share/udunits/udunits2.xml'"
      ],
      "metadata": {
        "id": "H-rIRb0vB9zs"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Descarga de datos"
      ],
      "metadata": {
        "id": "p0-D4-rZcltW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Descargaremos un fichero con datos del reanálisis ERA5 para experimentar con NCO\n",
        "\n",
        "* Los reanálisis ERA5 están disponibles gratuitamente en el [portal de C3S de Copernicus](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form)"
      ],
      "metadata": {
        "id": "Igynpmv0csCp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ifile = 'era5_pl_t_2020_europa_025.rar'\n",
        "!wget -q --no-check-certificate https://owncloud.aemet.es/index.php/s/yKz7WbTgcUQyuQW/download -O {ifile} && unrar -o+ e {ifile} && rm -f {ifile}\n",
        "!mv era5_pl_t_2020_europa_025.nc era5.nc"
      ],
      "metadata": {
        "id": "aJddORi14G0N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Operador **`nkcs`** (*kitchen sink / cajón de sastre*)"
      ],
      "metadata": {
        "id": "6hkDtxO1daM9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspección de un fichero"
      ],
      "metadata": {
        "id": "ksKNi7Zfg4tu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Información general sobre el fichero\n",
        "# -------------------------------------\n",
        "!ncks   era5.nc   |   head -n 50"
      ],
      "metadata": {
        "id": "mfpKbMii5hSM",
        "outputId": "b86ca2d2-0c8a-4d92-96d4-aa2fcf9d6183",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf era5 {\n",
            "  dimensions:\n",
            "    latitude = 161 ;\n",
            "    level = 37 ;\n",
            "    longitude = 161 ;\n",
            "    time = 12 ;\n",
            "\n",
            "  variables:\n",
            "    float latitude(latitude) ;\n",
            "      latitude:units = \"degrees_north\" ;\n",
            "      latitude:long_name = \"latitude\" ;\n",
            "\n",
            "    int level(level) ;\n",
            "      level:units = \"millibars\" ;\n",
            "      level:long_name = \"pressure_level\" ;\n",
            "\n",
            "    float longitude(longitude) ;\n",
            "      longitude:units = \"degrees_east\" ;\n",
            "      longitude:long_name = \"longitude\" ;\n",
            "\n",
            "    short t(time,level,latitude,longitude) ;\n",
            "      t:scale_factor = 0.00201365628767186 ;\n",
            "      t:add_offset = 251.995086921856 ;\n",
            "      t:_FillValue = -32767s ;\n",
            "      t:missing_value = -32767s ;\n",
            "      t:units = \"K\" ;\n",
            "      t:long_name = \"Temperature\" ;\n",
            "      t:standard_name = \"air_temperature\" ;\n",
            "\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  // global attributes:\n",
            "    :Conventions = \"CF-1.6\" ;\n",
            "    :history = \"2023-06-18 07:10:25 GMT by grib_to_netcdf-2.30.2: grib_to_netcdf era5_pl_t_2020_europa_025 -o era5_pl_t_2020_europa_025.nc\" ;\n",
            "\n",
            "\n",
            "  data:\n",
            "    latitude = 60, 59.75, 59.5, 59.25, 59, 58.75, 58.5, 58.25, 58, 57.75, 57.5, 57.25, 57, 56.75, 56.5, 56.25, 56, 55.75, 55.5, 55.25, 55, 54.75, 54.5, 54.25, 54, 53.75, 53.5, 53.25, 53, 52.75, 52.5, 52.25, 52, 51.75, 51.5, 51.25, 51, 50.75, 50.5, 50.25, 50, 49.75, 49.5, 49.25, 49, 48.75, 48.5, 48.25, 48, 47.75, 47.5, 47.25, 47, 46.75, 46.5, 46.25, 46, 45.75, 45.5, 45.25, 45, 44.75, 44.5, 44.25, 44, 43.75, 43.5, 43.25, 43, 42.75, 42.5, 42.25, 42, 41.75, 41.5, 41.25, 41, 40.75, 40.5, 40.25, 40, 39.75, 39.5, 39.25, 39, 38.75, 38.5, 38.25, 38, 37.75, 37.5, 37.25, 37, 36.75, 36.5, 36.25, 36, 35.75, 35.5, 35.25, 35, 34.75, 34.5, 34.25, 34, 33.75, 33.5, 33.25, 33, 32.75, 32.5, 32.25, 32, 31.75, 31.5, 31.25, 31, 30.75, 30.5, 30.25, 30, 29.75, 29.5, 29.25, 29, 28.75, 28.5, 28.25, 28, 27.75, 27.5, 27.25, 27, 26.75, 26.5, 26.25, 26, 25.75, 25.5, 25.25, 25, 24.75, 24.5, 24.25, 24, 23.75, 23.5, 23.25, 23, 22.75, 22.5, 22.25, 22, 21.75, 21.5, 21.25, 21, 20.75, 20.5, 20.25, 20 ;\n",
            "\n",
            "    level = 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000 ;\n",
            "\n",
            "    longitude = -30, -29.75, -29.5, -29.25, -29, -28.75, -28.5, -28.25, -28, -27.75, -27.5, -27.25, -27, -26.75, -26.5, -26.25, -26, -25.75, -25.5, -25.25, -25, -24.75, -24.5, -24.25, -24, -23.75, -23.5, -23.25, -23, -22.75, -22.5, -22.25, -22, -21.75, -21.5, -21.25, -21, -20.75, -20.5, -20.25, -20, -19.75, -19.5, -19.25, -19, -18.75, -18.5, -18.25, -18, -17.75, -17.5, -17.25, -17, -16.75, -16.5, -16.25, -16, -15.75, -15.5, -15.25, -15, -14.75, -14.5, -14.25, -14, -13.75, -13.5, -13.25, -13, -12.75, -12.5, -12.25, -12, -11.75, -11.5, -11.25, -11, -10.75, -10.5, -10.25, -10, -9.75, -9.5, -9.25, -9, -8.75, -8.5, -8.25, -8, -7.75, -7.5, -7.25, -7, -6.75, -6.5, -6.25, -6, -5.75, -5.5, -5.25, -5, -4.75, -4.5, -4.25, -4, -3.75, -3.5, -3.25, -3, -2.75, -2.5, -2.25, -2, -1.75, -1.5, -1.25, -1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 10 ;\n",
            "\n",
            "    t = \n",
            "    9117, 9194, 9334, 9515, 9712, 9897, 10045, 10135, 10153, 10092, 9956, 9753, 9501, 9220, 8931, 8655, 8406, 8195, 8026, 7901, 7815, 7763, 7738, 7733, 7745, 7770, 7809, 7862, 7927, 8003, 8091, 8189, 8294, 8399, 8496, 8579, 8644, 8687, 8708, 8704, 8673, 8614, 8525, 8410, 8272, 8117, 7950, 7780, 7616, 7468, 7341, 7237, 7158, 7104, 7072, 7055, 7045, 7033, 7010, 6969, 6903, 6810, 6691, 6550, 6396, 6234, 6074, 5926, 5800, 5705, 5645, 5621, 5636, 5687, 5767, 5866, 5975, 6090, 6205, 6318, 6425, 6522, 6608, 6679, 6736, 6776, 6796, 6790, 6753, 6682, 6578, 6446, 6296, 6135, 5970, 5809, 5658, 5521, 5400, 5296, 5210, 5145, 5106, 5103, 5143, 5232, 5371, 5552, 5759, 5968, 6149, 6272, 6320, 6294, 6211, 6107, 6032, 6033, 6151, 6408, 6812, 7350, 7988, 8678, 9359, 9975, 10484, 10856, 11084, 11176, 11160, 11070, 10933, 10767, 10582, 10388, 10187, 9974, 9739, 9476, 9185, 8871, 8536, 8172, 7752, 7229, 6566, 5776, 4937, 4162, 3534, 3071, 2751, 2573, 2576, 2823, 3354, 4161, 5196, 6380, 7610, \n",
            "    9252, 9404, 9586, 9770, 9928, 10035, 10073, 10034, 9918, 9735, 9499, 9229, 8948, 8675, 8427, 8214, 8042, 7913, 7824, 7769, 7742, 7738, 7750, 7775, 7811, 7857, 7913, 7978, 8051, 8128, 8209, 8293, 8374, 8447, 8505, 8542, 8557, 8548, 8514, 8455, 8372, 8262, 8131, 7982, 7821, 7656, 7495, 7344, 7212, 7104, 7025, 6971, 6940, 6927, 6927, 6933, 6935, 6924, 6894, 6839, 6759, 6654, 6526, 6383, 6235, 6090, 5956, 5840, 5749, 5686, 5651, 5643, 5657, 5690, 5735, 5784, 5832, 5877, 5921, 5965, 6013, 6062, 6112, 6163, 6214, 6260, 6295, 6312, 6303, 6264, 6197, 6105, 5993, 5866, 5733, 5597, 5463, 5331, 5204, 5085, 4977, 4888, 4829, 4816, 4863, 4974, 5149, 5377, 5638, 5898, 6120, 6271, 6334, 6310, 6222, 6113, 6034, 6042, 6175, 6453, 6872, 7416, 8048, 8715, 9356, 9918, 10363, 10674, 10849, 10899, 10847, 10723, 10552, 10349, 10123, 9882, 9632, 9374, 9104, 8816, 8512, 8193, 7859, 7504, 7107, 6638, 6072, 5420, 4735, 4095, 3561, 3158, 2891, 2776, 2853, 3163, 3722, 4500, 5429, 6425, 7414, \n",
            "    9459, 9625, 9782, 9899, 9956, 9939, 9847, 9686, 9471, 9222, 8960, 8702, 8466, 8262, 8096, 7965, 7868, 7801, 7760, 7742, 7741, 7757, 7787, 7831, 7886, 7950, 8020, 8094, 8167, 8236, 8297, 8349, 8390, 8414, 8418, 8397, 8353, 8288, 8202, 8097, 7973, 7834, 7685, 7529, 7373, 7224, 7089, 6973, 6880, 6811, 6766, 6743, 6734, 6733, 6733, 6728, 6711, 6674, 6615, 6533, 6431, 6315, 6191, 6066, 5952, 5856, 5784, 5739, 5723, 5734, 5766, 5811, 5862, 5910, 5950, 5977, 5986, 5980, 5960, 5933, 5904, 5876, 5851, 5830, 5814, 5799, 5780, 5751, 5708, 5651, 5581, 5500, 5411, 5317, 5222, 5127, 5030, 4929, 4825, 4724, 4632, 4563, 4532, 4559, 4657, 4834, 5083, 5388, 5720, 6039, 6299, 6463, 6517, 6470, 6349, 6204, 6094, 6080, 6204, 6483, 6907, 7446, 8059, 8693, 9288, 9793, 10177, 10427, 10545, 10544, 10444, 10269, 10041, 9778, 9495, 9201, 8905, 8611, 8320, 8035, 7755, 7477, 7198, 6915, 6621, 6291, 5891, 5407, 4866, 4332, 3866, 3513, 3296, 3243, 3385, 3742, 4296, 5000, 5782, 6573, 7328, \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Inspección de variables/dimensiones"
      ],
      "metadata": {
        "id": "r09uzQDBjhHY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Información sobre la dimensión latitud\n",
        "# ---------------------------------------\n",
        "!ncks  -v   latitude    era5.nc    |   head -n 50"
      ],
      "metadata": {
        "id": "b2VJVThSjeu3",
        "outputId": "7002e134-e717-4504-a922-f1615f4c4690",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf era5 {\n",
            "  dimensions:\n",
            "    latitude = 161 ;\n",
            "\n",
            "  variables:\n",
            "    float latitude(latitude) ;\n",
            "      latitude:units = \"degrees_north\" ;\n",
            "      latitude:long_name = \"latitude\" ;\n",
            "\n",
            "  data:\n",
            "    latitude = 60, 59.75, 59.5, 59.25, 59, 58.75, 58.5, 58.25, 58, 57.75, 57.5, 57.25, 57, 56.75, 56.5, 56.25, 56, 55.75, 55.5, 55.25, 55, 54.75, 54.5, 54.25, 54, 53.75, 53.5, 53.25, 53, 52.75, 52.5, 52.25, 52, 51.75, 51.5, 51.25, 51, 50.75, 50.5, 50.25, 50, 49.75, 49.5, 49.25, 49, 48.75, 48.5, 48.25, 48, 47.75, 47.5, 47.25, 47, 46.75, 46.5, 46.25, 46, 45.75, 45.5, 45.25, 45, 44.75, 44.5, 44.25, 44, 43.75, 43.5, 43.25, 43, 42.75, 42.5, 42.25, 42, 41.75, 41.5, 41.25, 41, 40.75, 40.5, 40.25, 40, 39.75, 39.5, 39.25, 39, 38.75, 38.5, 38.25, 38, 37.75, 37.5, 37.25, 37, 36.75, 36.5, 36.25, 36, 35.75, 35.5, 35.25, 35, 34.75, 34.5, 34.25, 34, 33.75, 33.5, 33.25, 33, 32.75, 32.5, 32.25, 32, 31.75, 31.5, 31.25, 31, 30.75, 30.5, 30.25, 30, 29.75, 29.5, 29.25, 29, 28.75, 28.5, 28.25, 28, 27.75, 27.5, 27.25, 27, 26.75, 26.5, 26.25, 26, 25.75, 25.5, 25.25, 25, 24.75, 24.5, 24.25, 24, 23.75, 23.5, 23.25, 23, 22.75, 22.5, 22.25, 22, 21.75, 21.5, 21.25, 21, 20.75, 20.5, 20.25, 20 ;\n",
            "\n",
            "} // group /\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Información sobre múltiples dimensiones\n",
        "# ---------------------------------------\n",
        "!ncks -v latitude,longitude,time  era5.nc  | head -n 50"
      ],
      "metadata": {
        "id": "F_zo4kNSkoSa",
        "outputId": "970547a8-f10c-4ae1-ca70-2e1987450468",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf era5 {\n",
            "  dimensions:\n",
            "    latitude = 161 ;\n",
            "    longitude = 161 ;\n",
            "    time = 12 ;\n",
            "\n",
            "  variables:\n",
            "    float latitude(latitude) ;\n",
            "      latitude:units = \"degrees_north\" ;\n",
            "      latitude:long_name = \"latitude\" ;\n",
            "\n",
            "    float longitude(longitude) ;\n",
            "      longitude:units = \"degrees_east\" ;\n",
            "      longitude:long_name = \"longitude\" ;\n",
            "\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  data:\n",
            "    latitude = 60, 59.75, 59.5, 59.25, 59, 58.75, 58.5, 58.25, 58, 57.75, 57.5, 57.25, 57, 56.75, 56.5, 56.25, 56, 55.75, 55.5, 55.25, 55, 54.75, 54.5, 54.25, 54, 53.75, 53.5, 53.25, 53, 52.75, 52.5, 52.25, 52, 51.75, 51.5, 51.25, 51, 50.75, 50.5, 50.25, 50, 49.75, 49.5, 49.25, 49, 48.75, 48.5, 48.25, 48, 47.75, 47.5, 47.25, 47, 46.75, 46.5, 46.25, 46, 45.75, 45.5, 45.25, 45, 44.75, 44.5, 44.25, 44, 43.75, 43.5, 43.25, 43, 42.75, 42.5, 42.25, 42, 41.75, 41.5, 41.25, 41, 40.75, 40.5, 40.25, 40, 39.75, 39.5, 39.25, 39, 38.75, 38.5, 38.25, 38, 37.75, 37.5, 37.25, 37, 36.75, 36.5, 36.25, 36, 35.75, 35.5, 35.25, 35, 34.75, 34.5, 34.25, 34, 33.75, 33.5, 33.25, 33, 32.75, 32.5, 32.25, 32, 31.75, 31.5, 31.25, 31, 30.75, 30.5, 30.25, 30, 29.75, 29.5, 29.25, 29, 28.75, 28.5, 28.25, 28, 27.75, 27.5, 27.25, 27, 26.75, 26.5, 26.25, 26, 25.75, 25.5, 25.25, 25, 24.75, 24.5, 24.25, 24, 23.75, 23.5, 23.25, 23, 22.75, 22.5, 22.25, 22, 21.75, 21.5, 21.25, 21, 20.75, 20.5, 20.25, 20 ;\n",
            "\n",
            "    longitude = -30, -29.75, -29.5, -29.25, -29, -28.75, -28.5, -28.25, -28, -27.75, -27.5, -27.25, -27, -26.75, -26.5, -26.25, -26, -25.75, -25.5, -25.25, -25, -24.75, -24.5, -24.25, -24, -23.75, -23.5, -23.25, -23, -22.75, -22.5, -22.25, -22, -21.75, -21.5, -21.25, -21, -20.75, -20.5, -20.25, -20, -19.75, -19.5, -19.25, -19, -18.75, -18.5, -18.25, -18, -17.75, -17.5, -17.25, -17, -16.75, -16.5, -16.25, -16, -15.75, -15.5, -15.25, -15, -14.75, -14.5, -14.25, -14, -13.75, -13.5, -13.25, -13, -12.75, -12.5, -12.25, -12, -11.75, -11.5, -11.25, -11, -10.75, -10.5, -10.25, -10, -9.75, -9.5, -9.25, -9, -8.75, -8.5, -8.25, -8, -7.75, -7.5, -7.25, -7, -6.75, -6.5, -6.25, -6, -5.75, -5.5, -5.25, -5, -4.75, -4.5, -4.25, -4, -3.75, -3.5, -3.25, -3, -2.75, -2.5, -2.25, -2, -1.75, -1.5, -1.25, -1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4, 4.25, 4.5, 4.75, 5, 5.25, 5.5, 5.75, 6, 6.25, 6.5, 6.75, 7, 7.25, 7.5, 7.75, 8, 8.25, 8.5, 8.75, 9, 9.25, 9.5, 9.75, 10 ;\n",
            "\n",
            "    time = 1051908, 1052652, 1053348, 1054092, 1054812, 1055556, 1056276, 1057020, 1057764, 1058484, 1059228, 1059948 ;\n",
            "\n",
            "} // group /\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Extracción de subconjuntos"
      ],
      "metadata": {
        "id": "sX2bar5Ho9Xz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extrae valores de la temperatura para:\n",
        "# - las 10 primeras latitudes\n",
        "# - las 10 primeras longitudees\n",
        "# - los dos primeros niveles verticales\n",
        "# ------------------------------------------------------\n",
        "!ncks -v  t   -d latitude,0,9    -d longitude,0,9   -d level,0,1    era5.nc | head -n 50"
      ],
      "metadata": {
        "id": "hKHRX33smBu3",
        "outputId": "ebd3512f-c831-48ff-d987-44b8aa389f19",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf era5 {\n",
            "  dimensions:\n",
            "    latitude = 10 ;\n",
            "    level = 2 ;\n",
            "    longitude = 10 ;\n",
            "    time = 12 ;\n",
            "\n",
            "  variables:\n",
            "    float latitude(latitude) ;\n",
            "      latitude:units = \"degrees_north\" ;\n",
            "      latitude:long_name = \"latitude\" ;\n",
            "\n",
            "    int level(level) ;\n",
            "      level:units = \"millibars\" ;\n",
            "      level:long_name = \"pressure_level\" ;\n",
            "\n",
            "    float longitude(longitude) ;\n",
            "      longitude:units = \"degrees_east\" ;\n",
            "      longitude:long_name = \"longitude\" ;\n",
            "\n",
            "    short t(time,level,latitude,longitude) ;\n",
            "      t:scale_factor = 0.00201365628767186 ;\n",
            "      t:add_offset = 251.995086921856 ;\n",
            "      t:_FillValue = -32767s ;\n",
            "      t:missing_value = -32767s ;\n",
            "      t:units = \"K\" ;\n",
            "      t:long_name = \"Temperature\" ;\n",
            "      t:standard_name = \"air_temperature\" ;\n",
            "\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  data:\n",
            "    latitude = 60, 59.75, 59.5, 59.25, 59, 58.75, 58.5, 58.25, 58, 57.75 ;\n",
            "\n",
            "    level = 1, 2 ;\n",
            "\n",
            "    longitude = -30, -29.75, -29.5, -29.25, -29, -28.75, -28.5, -28.25, -28, -27.75 ;\n",
            "\n",
            "    t = \n",
            "    9117, 9194, 9334, 9515, 9712, 9897, 10045, 10135, 10153, 10092, \n",
            "    9252, 9404, 9586, 9770, 9928, 10035, 10073, 10034, 9918, 9735, \n",
            "    9459, 9625, 9782, 9899, 9956, 9939, 9847, 9686, 9471, 9222, \n",
            "    9633, 9749, 9819, 9825, 9758, 9621, 9429, 9201, 8958, 8720, \n",
            "    9694, 9713, 9671, 9564, 9399, 9193, 8968, 8744, 8539, 8362, \n",
            "    9623, 9532, 9393, 9214, 9011, 8804, 8608, 8436, 8292, 8175, \n",
            "    9460, 9289, 9101, 8909, 8725, 8559, 8414, 8290, 8181, 8079, \n",
            "    9282, 9085, 8902, 8739, 8596, 8467, 8344, 8221, 8090, 7947, \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Volcado  a otro fichero (índices)"
      ],
      "metadata": {
        "id": "LG-_p108pt6b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Volcado de los niveles de presión a un nuevo fichero\n",
        "# ---------------------------------------------------------\n",
        "!ncks   -v   level    era5.nc   -O   resultado.nc\n",
        "\n",
        "\n",
        "# Comprobación\n",
        "# ---------------------------------\n",
        "!ncks   resultado.nc   | head -n 20"
      ],
      "metadata": {
        "id": "XbqmFXQPqS0X",
        "outputId": "5de9c390-4970-44dd-c32a-fb4a9426b2eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf resultado {\n",
            "  dimensions:\n",
            "    level = 37 ;\n",
            "\n",
            "  variables:\n",
            "    int level(level) ;\n",
            "      level:units = \"millibars\" ;\n",
            "      level:long_name = \"pressure_level\" ;\n",
            "\n",
            "  // global attributes:\n",
            "    :Conventions = \"CF-1.6\" ;\n",
            "    :history = \"Thu Jul  6 18:50:17 2023: ncks -v level era5.nc -O resultado.nc\\n\",\n",
            "      \"2023-06-18 07:10:25 GMT by grib_to_netcdf-2.30.2: grib_to_netcdf era5_pl_t_2020_europa_025 -o era5_pl_t_2020_europa_025.nc\" ;\n",
            "    :NCO = \"netCDF Operators version 5.1.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\" ;\n",
            "\n",
            "\n",
            "  data:\n",
            "    level = 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000 ;\n",
            "\n",
            "} // group /\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Volcado a otro fichero de la temperatura de un nivel (1000 hPa) en un recorte del área\n",
        "# ---------------------------------------------------------------------------------------\n",
        "!ncks -v t  -d level,36  -d longitude,10,20 -d latitude,10,20   era5.nc -O  resultado.nc\n",
        "\n",
        "# Comprobación\n",
        "# ------------------------------\n",
        "!ncks resultado.nc | head -n 55"
      ],
      "metadata": {
        "id": "nAlo0EOAyMvr",
        "outputId": "a06b63cf-37cf-4455-a7d2-100562d9aa87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf resultado {\n",
            "  dimensions:\n",
            "    latitude = 11 ;\n",
            "    level = 1 ;\n",
            "    longitude = 11 ;\n",
            "    time = 12 ;\n",
            "\n",
            "  variables:\n",
            "    float latitude(latitude) ;\n",
            "      latitude:units = \"degrees_north\" ;\n",
            "      latitude:long_name = \"latitude\" ;\n",
            "\n",
            "    int level(level) ;\n",
            "      level:units = \"millibars\" ;\n",
            "      level:long_name = \"pressure_level\" ;\n",
            "\n",
            "    float longitude(longitude) ;\n",
            "      longitude:units = \"degrees_east\" ;\n",
            "      longitude:long_name = \"longitude\" ;\n",
            "\n",
            "    short t(time,level,latitude,longitude) ;\n",
            "      t:scale_factor = 0.00201365628767186 ;\n",
            "      t:add_offset = 251.995086921856 ;\n",
            "      t:_FillValue = -32767s ;\n",
            "      t:missing_value = -32767s ;\n",
            "      t:units = \"K\" ;\n",
            "      t:long_name = \"Temperature\" ;\n",
            "      t:standard_name = \"air_temperature\" ;\n",
            "\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  // global attributes:\n",
            "    :Conventions = \"CF-1.6\" ;\n",
            "    :history = \"Thu Jul  6 18:50:18 2023: ncks -v t -d level,36 -d longitude,10,20 -d latitude,10,20 era5.nc -O resultado.nc\\n\",\n",
            "      \"2023-06-18 07:10:25 GMT by grib_to_netcdf-2.30.2: grib_to_netcdf era5_pl_t_2020_europa_025 -o era5_pl_t_2020_europa_025.nc\" ;\n",
            "    :NCO = \"netCDF Operators version 5.1.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\" ;\n",
            "\n",
            "\n",
            "  data:\n",
            "    latitude = 57.5, 57.25, 57, 56.75, 56.5, 56.25, 56, 55.75, 55.5, 55.25, 55 ;\n",
            "\n",
            "    level = 1000 ;\n",
            "\n",
            "    longitude = -27.5, -27.25, -27, -26.75, -26.5, -26.25, -26, -25.75, -25.5, -25.25, -25 ;\n",
            "\n",
            "    t = \n",
            "    12984, 13075, 13230, 13439, 13662, 13855, 13984, 14041, 14047, 14028, 14003, \n",
            "    13025, 13178, 13387, 13610, 13798, 13918, 13965, 13959, 13936, 13923, 13929, \n",
            "    13143, 13356, 13582, 13762, 13868, 13905, 13898, 13880, 13879, 13906, 13957, \n",
            "    13335, 13561, 13735, 13826, 13849, 13842, 13833, 13847, 13896, 13980, 14086, \n",
            "    13538, 13702, 13785, 13801, 13794, 13801, 13836, 13905, 14014, 14157, 14307, \n",
            "    13653, 13725, 13742, 13742, 13766, 13837, 13945, 14077, 14223, 14375, 14510, \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Volcado  a otro fichero (valores)"
      ],
      "metadata": {
        "id": "eh49M3q-z5Mz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Para realizar la misma operación especificando valores en lugar de índices se usa el comando **`ncea`**\n",
        "\n",
        "* `ncea` = Ensemble Averager\n",
        "\n",
        "* La sintaxis de los modificadores se mantiene"
      ],
      "metadata": {
        "id": "YLRYCMcW0qiL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Volcado a otro fichero de la temperatura en el nivel de 500 hPa en una ventana\n",
        "# de latitud [30N, 40N] y longitud [-30,-20]\n",
        "# ---------------------------------------------------------------------------------------\n",
        "!ncea   -v t    -d level,500.0    -d longitude,-30.,-25.  -d latitude,35.,40.    era5.nc   -O  resultado.nc\n",
        "\n",
        "# Comprobación\n",
        "# ------------------------------\n",
        "!ncks resultado.nc | head -n 50"
      ],
      "metadata": {
        "id": "L8axrcCXzB1o",
        "outputId": "b95be2ea-4e08-4985-c73b-9114d6a4f4fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf resultado {\n",
            "  dimensions:\n",
            "    latitude = 21 ;\n",
            "    level = 1 ;\n",
            "    longitude = 21 ;\n",
            "    time = 12 ;\n",
            "\n",
            "  variables:\n",
            "    float latitude(latitude) ;\n",
            "      latitude:units = \"degrees_north\" ;\n",
            "      latitude:long_name = \"latitude\" ;\n",
            "\n",
            "    int level(level) ;\n",
            "      level:units = \"millibars\" ;\n",
            "      level:long_name = \"pressure_level\" ;\n",
            "\n",
            "    float longitude(longitude) ;\n",
            "      longitude:units = \"degrees_east\" ;\n",
            "      longitude:long_name = \"longitude\" ;\n",
            "\n",
            "    double t(time,level,latitude,longitude) ;\n",
            "      t:_FillValue = -32767. ;\n",
            "      t:missing_value = -32767s ;\n",
            "      t:units = \"K\" ;\n",
            "      t:long_name = \"Temperature\" ;\n",
            "      t:standard_name = \"air_temperature\" ;\n",
            "\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  // global attributes:\n",
            "    :Conventions = \"CF-1.6\" ;\n",
            "    :history = \"Thu Jul  6 18:50:18 2023: ncea -v t -d level,500.0 -d longitude,-30.,-25. -d latitude,35.,40. era5.nc -O resultado.nc\\n\",\n",
            "      \"2023-06-18 07:10:25 GMT by grib_to_netcdf-2.30.2: grib_to_netcdf era5_pl_t_2020_europa_025 -o era5_pl_t_2020_europa_025.nc\" ;\n",
            "    :NCO = \"netCDF Operators version 5.1.6 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)\" ;\n",
            "\n",
            "\n",
            "  data:\n",
            "    latitude = 40, 39.75, 39.5, 39.25, 39, 38.75, 38.5, 38.25, 38, 37.75, 37.5, 37.25, 37, 36.75, 36.5, 36.25, 36, 35.75, 35.5, 35.25, 35 ;\n",
            "\n",
            "    level = 500 ;\n",
            "\n",
            "    longitude = -30, -29.75, -29.5, -29.25, -29, -28.75, -28.5, -28.25, -28, -27.75, -27.5, -27.25, -27, -26.75, -26.5, -26.25, -26, -25.75, -25.5, -25.25, -25 ;\n",
            "\n",
            "    t = \n",
            "    255.510930800131, 255.831102149871, 256.205642219378, 256.568100351159, 256.888271700899, 257.162128956022, 257.327248771611, 257.363494584789, 257.301071239872, 257.135951424282, 256.914449232639, 256.819807387118, 256.982913546419, 257.293016614721, 257.542709994392, 257.637351839913, 257.57090118242, 257.341344365625, 257.027213984748, 256.80973910568, 256.781547917652, \n",
            "    255.821033868433, 256.181478343926, 256.529840881693, 256.839943949995, 257.115814861406, 257.313153177598, 257.383631147666, 257.343358021913, 257.1983747692, 256.972845264981, 256.829875668556, 256.934585795515, 257.228579613515, 257.524587087803, 257.689706903392, 257.669570340515, 257.488341274625, 257.234620582378, 257.007077421871, 256.894312669762, 256.932572139228, \n",
            "    256.199601250515, 256.535881850556, 256.829875668556, 257.089637329666, 257.31113952131, 257.433972554858, 257.42390427342, 257.293016614721, 257.053391516488, 256.84397126257, 256.890285357186, 257.196361112913, 257.524587087803, 257.734007341721, 257.794417030351, 257.649433777639, 257.363494584789, 257.133937767995, 257.041309578762, 257.037282266186, 257.123869486556, \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Operador ncrcat (concatenador)"
      ],
      "metadata": {
        "id": "gORqHrDp9nPI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Concatenación de tiempos"
      ],
      "metadata": {
        "id": "17CqC9mhuLYH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Separamos los datos del año 2020 en dos ficheros semestrales con **`ncks`**"
      ],
      "metadata": {
        "id": "b0Th__9S-AMr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -f enero_junio.nc* julio_diciembre.nc* resultado.nc*"
      ],
      "metadata": {
        "id": "D_cQaexOCqdo"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# El fichero era5.nc contiene un volumen\n",
        "# atmosférico f=f(level,lat,lon) por cada mes del año 2020\n",
        "# ---------------------------------------------------------------\n",
        "!ncks -v time era5.nc\n",
        "\n",
        "print('==================================')\n",
        "\n",
        "# Separamos los primeros 6 meses en un fichero\n",
        "# ---------------------------------------------\n",
        "!ncks -d time,2020-01-01,2020-06-30 era5.nc -O  dumb.nc\n",
        "\n",
        "\n",
        "# Hacemos la dimensión temporal ilimitada\n",
        "# -------------------------------------------------------\n",
        "!ncks --mk_rec_dmn time    dumb.nc    -O enero_junio.nc\n",
        "\n",
        "\n",
        "# Separamos los últimos 6 meses en otro fichero\n",
        "# -----------------------------------------------\n",
        "!ncks -d time,2020-07-01,2020-12-31 era5.nc  -O julio_diciembre.nc\n",
        "\n",
        "\n",
        "# Comprobación\n",
        "# ------------------------------\n",
        "!ncks -v time enero_junio.nc\n",
        "print('==================================')\n",
        "!ncks -v time julio_diciembre.nc"
      ],
      "metadata": {
        "id": "tswFeoj07O-s",
        "outputId": "3e190c53-7c21-4f5a-9fe0-919f9c9a8bb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf era5 {\n",
            "  dimensions:\n",
            "    time = 12 ;\n",
            "\n",
            "  variables:\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  data:\n",
            "    time = 1051908, 1052652, 1053348, 1054092, 1054812, 1055556, 1056276, 1057020, 1057764, 1058484, 1059228, 1059948 ;\n",
            "\n",
            "} // group /\n",
            "==================================\n",
            "netcdf enero_junio {\n",
            "  dimensions:\n",
            "    time = UNLIMITED ; // (6 currently)\n",
            "\n",
            "  variables:\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  data:\n",
            "    time = 1051908, 1052652, 1053348, 1054092, 1054812, 1055556 ;\n",
            "\n",
            "} // group /\n",
            "==================================\n",
            "netcdf julio_diciembre {\n",
            "  dimensions:\n",
            "    time = 6 ;\n",
            "\n",
            "  variables:\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  data:\n",
            "    time = 1056276, 1057020, 1057764, 1058484, 1059228, 1059948 ;\n",
            "\n",
            "} // group /\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Y a continuación los volvemos a concatenar con **`ncrcat`**"
      ],
      "metadata": {
        "id": "quyN2XT9-Zmb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# El fichero era5.nc contiene un dato por cada mes\n",
        "# ------------------------------------------------\n",
        "!ncrcat enero_junio.nc julio_diciembre.nc -O  resultado.nc\n",
        "\n",
        "\n",
        "# Comprobación\n",
        "# ------------------------------\n",
        "!ncks -v time  resultado.nc  | head -n 50"
      ],
      "metadata": {
        "id": "l16n7MwL-vJf",
        "outputId": "1c31753c-a822-4027-838e-3ef281cb53f6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "netcdf resultado {\n",
            "  dimensions:\n",
            "    time = UNLIMITED ; // (12 currently)\n",
            "\n",
            "  variables:\n",
            "    int time(time) ;\n",
            "      time:units = \"hours since 1900-01-01 00:00:00.0\" ;\n",
            "      time:long_name = \"time\" ;\n",
            "      time:calendar = \"gregorian\" ;\n",
            "\n",
            "  data:\n",
            "    time = 1051908, 1052652, 1053348, 1054092, 1054812, 1055556, 1056276, 1057020, 1057764, 1058484, 1059228, 1059948 ;\n",
            "\n",
            "} // group /\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Más herramientas\n",
        "\n",
        "* [Acceso a datos NetCDF de Copernicus/ECMWF](https://colab.research.google.com/github/waveology/kitchen/blob/main/00_acceso_a_datos_netcdf_copernicus_ecmwf.ipynb)\n",
        "\n",
        "* [Introducción al trabajo con NetCDF](https://colab.research.google.com/github/waveology/kitchen/blob/main/01_introduccion_al_trabajo_con_netcdf.ipynb)\n",
        "\n",
        "* [Introducción a cdo](https://colab.research.google.com/github/waveology/kitchen/blob/main/02_introduccion_cdo.ipynb)\n",
        "\n",
        "* [Introducción a xarray](https://colab.research.google.com/github/waveology/kitchen/blob/main/03_introduccion_xarray.ipynb)\n",
        "\n",
        "* [Introducción a Cartopy](https://colab.research.google.com/github/waveology/kitchen/blob/main/04_introduccion_cartopy.ipynb)\n",
        "\n",
        "* [Introducción a Netcdf4](https://colab.research.google.com/github/waveology/kitchen/blob/main/05_introduccion_netcdf4.ipynb)\n"
      ],
      "metadata": {
        "id": "838lC0oIxi6Y"
      }
    }
  ]
}