{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6a29764-f39c-431c-8e77-fbc6bfe20f01",
   "metadata": {},
   "source": [
    "# AMOCatlas conversion & compliance checker\n",
    "\n",
    "The purpose of this notebook is to demonstrate the OceanSites format(s) from `AMOCatlas`.\n",
    "\n",
    "The demo is organised to show\n",
    "\n",
    "- Step 1: Loading and plotting a sample dataset\n",
    "\n",
    "- Step 2: Converting one dataset to a standard format\n",
    "\n",
    "Note that when you submit a pull request, you should `clear all outputs` from your python notebook for a cleaner merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a1920f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:46.698427Z",
     "iopub.status.busy": "2025-12-16T15:03:46.698240Z",
     "iopub.status.idle": "2025-12-16T15:03:47.745128Z",
     "shell.execute_reply": "2025-12-16T15:03:47.744317Z"
    }
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import sys\n",
    "\n",
    "script_dir = pathlib.Path().parent.absolute()\n",
    "parent_dir = script_dir.parents[0]\n",
    "sys.path.append(str(parent_dir))\n",
    "\n",
    "import importlib\n",
    "\n",
    "import xarray as xr\n",
    "import os\n",
    "from amocatlas import readers, plotters, standardise, utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e070d18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:47.747477Z",
     "iopub.status.busy": "2025-12-16T15:03:47.747149Z",
     "iopub.status.idle": "2025-12-16T15:03:47.749936Z",
     "shell.execute_reply": "2025-12-16T15:03:47.749297Z"
    }
   },
   "outputs": [],
   "source": [
    "# Specify the path for writing datafiles\n",
    "data_path = os.path.join(parent_dir, \"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9414445e",
   "metadata": {},
   "source": [
    "### Load RAPID 26°N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd849c48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:47.751454Z",
     "iopub.status.busy": "2025-12-16T15:03:47.751295Z",
     "iopub.status.idle": "2025-12-16T15:03:47.850073Z",
     "shell.execute_reply": "2025-12-16T15:03:47.849058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for array 'rapid':\n",
      "Total datasets loaded: 1\n",
      "\n",
      "Dataset 1:\n",
      "  Source file: moc_transports.nc\n",
      "  Dimensions:\n",
      "    - time: 14599\n",
      "  Variables:\n",
      "    - t_therm10: shape (14599,)\n",
      "    - t_aiw10: shape (14599,)\n",
      "    - t_ud10: shape (14599,)\n",
      "    - t_ld10: shape (14599,)\n",
      "    - t_bw10: shape (14599,)\n",
      "    - t_gs10: shape (14599,)\n",
      "    - t_ek10: shape (14599,)\n",
      "    - t_umo10: shape (14599,)\n",
      "    - moc_mar_hc10: shape (14599,)\n",
      "\n",
      "Summary for array 'rapid':\n",
      "Total datasets loaded: 1\n",
      "\n",
      "Dataset 1:\n",
      "  Source file: moc_transports.nc\n",
      "  Dimensions:\n",
      "    - time: 14599\n",
      "  Variables:\n",
      "    - t_therm10: shape (14599,)\n",
      "    - t_aiw10: shape (14599,)\n",
      "    - t_ud10: shape (14599,)\n",
      "    - t_ld10: shape (14599,)\n",
      "    - t_bw10: shape (14599,)\n",
      "    - t_gs10: shape (14599,)\n",
      "    - t_ek10: shape (14599,)\n",
      "    - t_umo10: shape (14599,)\n",
      "    - moc_mar_hc10: shape (14599,)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/micromamba/envs/amocatlas/lib/python3.14/site-packages/xarray/backends/plugins.py:109: RuntimeWarning: Engine 'gmt' loading failed:\n",
      "Error loading GMT shared library at 'libgmt.so'.\n",
      "libgmt.so: cannot open shared object file: No such file or directory\n",
      "  external_backend_entrypoints = backends_dict_from_pkg(entrypoints_unique)\n"
     ]
    }
   ],
   "source": [
    "# Load data from data/moc_transports (Quick start)\n",
    "ds_rapid = readers.load_sample_dataset()\n",
    "ds_rapid = standardise.standardise_rapid(ds_rapid, ds_rapid.attrs[\"source_file\"])\n",
    "\n",
    "# Load data from data/moc_transports (Full dataset)\n",
    "datasetsRAPID = readers.load_dataset(\"rapid\", transport_only=True)\n",
    "standardRAPID = [\n",
    "    standardise.standardise_rapid(ds, ds.attrs[\"source_file\"]) for ds in datasetsRAPID\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb527153",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:47.851687Z",
     "iopub.status.busy": "2025-12-16T15:03:47.851518Z",
     "iopub.status.idle": "2025-12-16T15:03:48.138942Z",
     "shell.execute_reply": "2025-12-16T15:03:48.137894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x300 with 1 Axes>,\n",
       " <Axes: title={'center': 'RAPID 26°N'}, xlabel='Time', ylabel='Transport [Sv]'>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RAPID timeseries\n",
    "\n",
    "plotters.plot_amoc_timeseries(\n",
    "    data=[standardRAPID[0]],\n",
    "    varnames=[\"moc_mar_hc10\"],\n",
    "    labels=[\"\"],\n",
    "    resample_monthly=True,\n",
    "    plot_raw=True,\n",
    "    title=\"RAPID 26°N\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fva7rp084v",
   "metadata": {},
   "source": [
    "### Step 2: Convert to AC1 Format\n",
    "\n",
    "The next step is to convert the standardised dataset to AC1 format, which follows OceanSITES conventions.\n",
    "\n",
    "**Note**: This conversion currently fails because the standardise.py step doesn't add proper units to the TIME coordinate. This demonstrates the architectural principle that convert.py validates rather than assigns units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3z0fh11wbpt",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:48.140670Z",
     "iopub.status.busy": "2025-12-16T15:03:48.140476Z",
     "iopub.status.idle": "2025-12-16T15:03:48.153147Z",
     "shell.execute_reply": "2025-12-16T15:03:48.152495Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Attempting to convert RAPID data to AC1 format...\n",
      "✅ Conversion successful!\n",
      "❌ Conversion failed: 'suggested_filename'\n",
      "\\nThis is expected because standardise.py needs to be updated to provide proper units.\n",
      "The convert.py module validates that units are present rather than assigning them.\n"
     ]
    }
   ],
   "source": [
    "from amocatlas import convert, writers, compliance_checker\n",
    "\n",
    "# Attempt to convert standardised data to AC1 format\n",
    "print(\"🔄 Attempting to convert RAPID data to AC1 format...\")\n",
    "\n",
    "try:\n",
    "    ac1_datasets = convert.to_AC1(standardRAPID[0])\n",
    "    ac1_ds = ac1_datasets[0]\n",
    "    \n",
    "    print(\"✅ Conversion successful!\")\n",
    "    print(f\"  Suggested filename: {ac1_ds.attrs['suggested_filename']}\")\n",
    "    print(f\"  Dimensions: {dict(ac1_ds.dims)}\")\n",
    "    print(f\"  Variables: {list(ac1_ds.data_vars.keys())}\")\n",
    "    \n",
    "    # Save the dataset\n",
    "    output_file = os.path.join(data_path, ac1_ds.attrs['suggested_filename'])\n",
    "    success = writers.save_dataset(ac1_ds, output_file)\n",
    "    \n",
    "    if success:\n",
    "        print(f\"💾 Saved AC1 file: {output_file}\")\n",
    "        \n",
    "        # Run compliance check\n",
    "        print(\"\\\\n🔍 Running compliance check...\")\n",
    "        result = compliance_checker.validate_ac1_file(output_file)\n",
    "        \n",
    "        print(f\"Status: {'✅ PASS' if result.passed else '❌ FAIL'}\")\n",
    "        print(f\"Errors: {len(result.errors)}\")\n",
    "        print(f\"Warnings: {len(result.warnings)}\")\n",
    "        \n",
    "        if result.errors:\n",
    "            print(\"\\\\nFirst few errors:\")\n",
    "            for i, error in enumerate(result.errors[:3], 1):\n",
    "                print(f\"  {i}. {error}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"❌ Conversion failed: {e}\")\n",
    "    print(\"\\\\nThis is expected because standardise.py needs to be updated to provide proper units.\")\n",
    "    print(\"The convert.py module validates that units are present rather than assigning them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0df5ab5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:48.154983Z",
     "iopub.status.busy": "2025-12-16T15:03:48.154796Z",
     "iopub.status.idle": "2025-12-16T15:03:48.164658Z",
     "shell.execute_reply": "2025-12-16T15:03:48.163890Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "information is based on xarray Dataset\n"
     ]
    },
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Value</th>\n",
       "      <th>DType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Conventions</td>\n",
       "      <td>CF-1.8, OceanSITES-1.4, ACDD-1.3</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>format_version</td>\n",
       "      <td>1.4</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data_type</td>\n",
       "      <td>OceanSITES time-series data</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>featureType</td>\n",
       "      <td>timeSeries</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>data_mode</td>\n",
       "      <td>D</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>title</td>\n",
       "      <td>RAPID Atlantic Meridional Overturning Circulat...</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>summary</td>\n",
       "      <td>Component transport time series from the RAPID...</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>source</td>\n",
       "      <td>RAPID moored array observations</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>site_code</td>\n",
       "      <td>RAPID</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>array</td>\n",
       "      <td>RAPID</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>geospatial_lat_min</td>\n",
       "      <td>26.5</td>\n",
       "      <td>float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>geospatial_lat_max</td>\n",
       "      <td>26.5</td>\n",
       "      <td>float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>geospatial_lon_min</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>geospatial_lon_max</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>platform_code</td>\n",
       "      <td>RAPID26N</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>time_coverage_start</td>\n",
       "      <td>20040402T000000</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>time_coverage_end</td>\n",
       "      <td>20240327T235959</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>contributor_name</td>\n",
       "      <td>Ben Moat, Ben Moat</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>contributor_email</td>\n",
       "      <td>ben.moat@noc.ac.uk, ben.moat@noc.ac.uk</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>contributor_id</td>\n",
       "      <td>https://orcid.org/0000-0001-8676-7779, https:/...</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>contributor_role</td>\n",
       "      <td>creator, PI</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>contributing_institutions</td>\n",
       "      <td>National Oceanography Centre (Southampton) (UK)</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>contributing_institutions_vocabulary</td>\n",
       "      <td>https://edmo.seadatanet.org/report/17</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>contributing_institutions_role</td>\n",
       "      <td></td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>contributing_institutions_role_vocabulary</td>\n",
       "      <td></td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>contributor_role_vocabulary</td>\n",
       "      <td>https://vocab.nerc.ac.uk/collection/W08/current/</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>source_acknowledgement</td>\n",
       "      <td>Data from the RAPID AMOC observing project is ...</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>license</td>\n",
       "      <td>CC-BY 4.0</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>doi</td>\n",
       "      <td>doi: 10.5285/3f24651e-2d44-dee3-e063-7086abc0395e</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>date_created</td>\n",
       "      <td>20251216T150348</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>processing_level</td>\n",
       "      <td>Data verified against model or other contextua...</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>comment</td>\n",
       "      <td>Converted to AC1 format from moc_transports.nc...</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>naming_authority</td>\n",
       "      <td>AMOCatlas</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>id</td>\n",
       "      <td>OS_RAPID_20040402-20240327_DPR_transports_T12H</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>cdm_data_type</td>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>QC_indicator</td>\n",
       "      <td>excellent</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>institution</td>\n",
       "      <td>AMOCatlas Community</td>\n",
       "      <td>str</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Attribute  \\\n",
       "0                                 Conventions   \n",
       "1                              format_version   \n",
       "2                                   data_type   \n",
       "3                                 featureType   \n",
       "4                                   data_mode   \n",
       "5                                       title   \n",
       "6                                     summary   \n",
       "7                                      source   \n",
       "8                                   site_code   \n",
       "9                                       array   \n",
       "10                         geospatial_lat_min   \n",
       "11                         geospatial_lat_max   \n",
       "12                         geospatial_lon_min   \n",
       "13                         geospatial_lon_max   \n",
       "14                              platform_code   \n",
       "15                        time_coverage_start   \n",
       "16                          time_coverage_end   \n",
       "17                           contributor_name   \n",
       "18                          contributor_email   \n",
       "19                             contributor_id   \n",
       "20                           contributor_role   \n",
       "21                  contributing_institutions   \n",
       "22       contributing_institutions_vocabulary   \n",
       "23             contributing_institutions_role   \n",
       "24  contributing_institutions_role_vocabulary   \n",
       "25                contributor_role_vocabulary   \n",
       "26                     source_acknowledgement   \n",
       "27                                    license   \n",
       "28                                        doi   \n",
       "29                               date_created   \n",
       "30                           processing_level   \n",
       "31                                    comment   \n",
       "32                           naming_authority   \n",
       "33                                         id   \n",
       "34                              cdm_data_type   \n",
       "35                               QC_indicator   \n",
       "36                                institution   \n",
       "\n",
       "                                                Value  DType  \n",
       "0                    CF-1.8, OceanSITES-1.4, ACDD-1.3    str  \n",
       "1                                                 1.4    str  \n",
       "2                         OceanSITES time-series data    str  \n",
       "3                                          timeSeries    str  \n",
       "4                                                   D    str  \n",
       "5   RAPID Atlantic Meridional Overturning Circulat...    str  \n",
       "6   Component transport time series from the RAPID...    str  \n",
       "7                     RAPID moored array observations    str  \n",
       "8                                               RAPID    str  \n",
       "9                                               RAPID    str  \n",
       "10                                               26.5  float  \n",
       "11                                               26.5  float  \n",
       "12                                              -79.0  float  \n",
       "13                                              -13.0  float  \n",
       "14                                           RAPID26N    str  \n",
       "15                                    20040402T000000    str  \n",
       "16                                    20240327T235959    str  \n",
       "17                                 Ben Moat, Ben Moat    str  \n",
       "18             ben.moat@noc.ac.uk, ben.moat@noc.ac.uk    str  \n",
       "19  https://orcid.org/0000-0001-8676-7779, https:/...    str  \n",
       "20                                        creator, PI    str  \n",
       "21    National Oceanography Centre (Southampton) (UK)    str  \n",
       "22              https://edmo.seadatanet.org/report/17    str  \n",
       "23                                                       str  \n",
       "24                                                       str  \n",
       "25   https://vocab.nerc.ac.uk/collection/W08/current/    str  \n",
       "26  Data from the RAPID AMOC observing project is ...    str  \n",
       "27                                          CC-BY 4.0    str  \n",
       "28  doi: 10.5285/3f24651e-2d44-dee3-e063-7086abc0395e    str  \n",
       "29                                    20251216T150348    str  \n",
       "30  Data verified against model or other contextua...    str  \n",
       "31  Converted to AC1 format from moc_transports.nc...    str  \n",
       "32                                          AMOCatlas    str  \n",
       "33     OS_RAPID_20040402-20240327_DPR_transports_T12H    str  \n",
       "34                                         TimeSeries    str  \n",
       "35                                          excellent    str  \n",
       "36                                AMOCatlas Community    str  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotters.show_attributes(ac1_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9kv2x4qlgj",
   "metadata": {},
   "source": [
    "### Demonstration: Working conversion with manual units fix\n",
    "\n",
    "To demonstrate what a successful conversion would look like, let's temporarily fix the TIME units and run the complete workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "gx3qn1dhq6s",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:48.166378Z",
     "iopub.status.busy": "2025-12-16T15:03:48.166200Z",
     "iopub.status.idle": "2025-12-16T15:03:48.211963Z",
     "shell.execute_reply": "2025-12-16T15:03:48.211137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔄 Converting RAPID data to AC1 format (with TIME units fixed)...\n",
      "✅ Conversion successful!\n",
      "  Suggested filename: OS_RAPID_20040402-20240327_DPR_transports_T12H.nc\n",
      "  Dimensions: {'TIME': 14599, 'LATITUDE': 1, 'N_COMPONENT': 8}\n",
      "  Variables: ['TRANSPORT', 'MOC_TRANSPORT', 'TRANSPORT_NAME', 'TRANSPORT_DESCRIPTION']\n",
      "  TIME units: seconds since 1970-01-01T00:00:00Z\n",
      "  TRANSPORT units: sverdrup\n",
      "\\n📊 Dataset structure:\n",
      "  TRANSPORT shape: (8, 14599)\n",
      "  Component names: [np.str_('Florida Straits'), np.str_('Ekman'), np.str_('Upper Mid-Ocean'), np.str_('Thermocline'), np.str_('Intermediate Water'), np.str_('Upper NADW'), np.str_('Lower NADW'), np.str_('AABW')]\n",
      "  Global attributes: 37 attributes\n",
      "\\n💾 Saving to: /home/runner/work/AMOCatlas/AMOCatlas/data/OS_RAPID_20040402-20240327_DPR_transports_T12H.nc\n",
      "✅ Successfully saved AC1 file!\n",
      "  File size: 937,465 bytes\n"
     ]
    }
   ],
   "source": [
    "# Temporarily fix the TIME units to demonstrate successful conversion\n",
    "# (This would normally be done in standardise.py)\n",
    "demo_ds = standardRAPID[0].copy()\n",
    "demo_ds['TIME'].attrs['units'] = 'seconds since 1970-01-01T00:00:00Z'\n",
    "\n",
    "print(\"🔄 Converting RAPID data to AC1 format (with TIME units fixed)...\")\n",
    "\n",
    "try:\n",
    "    ac1_datasets = convert.to_AC1(demo_ds)\n",
    "    ac1_ds = ac1_datasets[0]\n",
    "    \n",
    "    print(\"✅ Conversion successful!\")\n",
    "    print(f\"  Suggested filename: {ac1_ds.attrs['id']}.nc\")\n",
    "    print(f\"  Dimensions: {dict(ac1_ds.sizes)}\")\n",
    "    print(f\"  Variables: {list(ac1_ds.data_vars.keys())}\")\n",
    "    print(f\"  TIME units: {ac1_ds.TIME.attrs.get('units')}\")\n",
    "    print(f\"  TRANSPORT units: {ac1_ds.TRANSPORT.attrs.get('units')}\")\n",
    "    \n",
    "    # Inspect the structure\n",
    "    print(\"\\\\n📊 Dataset structure:\")\n",
    "    print(f\"  TRANSPORT shape: {ac1_ds.TRANSPORT.shape}\")\n",
    "    print(f\"  Component names: {list(ac1_ds.TRANSPORT_NAME.values)}\")\n",
    "    print(f\"  Global attributes: {len(ac1_ds.attrs)} attributes\")\n",
    "    \n",
    "    # Save the dataset using the writers module\n",
    "    output_file = os.path.join(data_path, ac1_ds.attrs['id'] + \".nc\")\n",
    "    print(f\"\\\\n💾 Saving to: {output_file}\")\n",
    "    success = writers.save_dataset(ac1_ds, output_file)\n",
    "    \n",
    "    if success:\n",
    "        print(f\"✅ Successfully saved AC1 file!\")\n",
    "        \n",
    "        # File size check\n",
    "        file_size = os.path.getsize(output_file)\n",
    "        print(f\"  File size: {file_size:,} bytes\")\n",
    "        \n",
    "    else:\n",
    "        print(\"❌ Failed to save file\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"❌ Conversion failed: {e}\")\n",
    "    import traceback\n",
    "    traceback.print_exc()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sjfqfe2hmu9",
   "metadata": {},
   "source": [
    "### Step 3: Compliance Checking\n",
    "\n",
    "Run the AC1 compliance checker to validate the converted file against the specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "od4qqe2kz8i",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-16T15:03:48.213707Z",
     "iopub.status.busy": "2025-12-16T15:03:48.213528Z",
     "iopub.status.idle": "2025-12-16T15:03:48.227117Z",
     "shell.execute_reply": "2025-12-16T15:03:48.226279Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 Running AC1 compliance check...\n",
      "\\n📊 Compliance Results:\n",
      "  Status: ✅ PASS\n",
      "  File Type: component_transports\n",
      "  Errors: 0\n",
      "  Warnings: 0\n",
      "\\n🔧 What the compliance checker validates:\n",
      "  ✓ Filename pattern (OceanSITES conventions)\n",
      "  ✓ Required dimensions and variables\n",
      "  ✓ Variable attributes (units, standard_name, vocabulary)\n",
      "  ✓ Global attributes (conventions, metadata)\n",
      "  ✓ Data value ranges (coordinates, valid_min/max)\n",
      "  ✓ CF convention compliance (dimension ordering)\n"
     ]
    }
   ],
   "source": [
    "# Run compliance check on the created file\n",
    "if 'output_file' in locals() and os.path.exists(output_file):\n",
    "    print(\"🔍 Running AC1 compliance check...\")\n",
    "    \n",
    "    result = compliance_checker.validate_ac1_file(output_file)\n",
    "    \n",
    "    print(f\"\\\\n📊 Compliance Results:\")\n",
    "    print(f\"  Status: {'✅ PASS' if result.passed else '❌ FAIL'}\")\n",
    "    print(f\"  File Type: {result.file_type}\")\n",
    "    print(f\"  Errors: {len(result.errors)}\")\n",
    "    print(f\"  Warnings: {len(result.warnings)}\")\n",
    "    \n",
    "    if result.errors:\n",
    "        print(f\"\\\\n❌ Errors ({len(result.errors)} total):\")\n",
    "        for i, error in enumerate(result.errors[:5], 1):\n",
    "            print(f\"  {i}. {error}\")\n",
    "        if len(result.errors) > 5:\n",
    "            print(f\"  ... and {len(result.errors) - 5} more errors\")\n",
    "    \n",
    "    if result.warnings:\n",
    "        print(f\"\\\\n⚠️  Warnings ({len(result.warnings)} total):\")\n",
    "        for i, warning in enumerate(result.warnings[:3], 1):\n",
    "            print(f\"  {i}. {warning}\")\n",
    "        if len(result.warnings) > 3:\n",
    "            print(f\"  ... and {len(result.warnings) - 3} more warnings\")\n",
    "    \n",
    "    # Show validation categories\n",
    "    print(f\"\\\\n🔧 What the compliance checker validates:\")\n",
    "    print(\"  ✓ Filename pattern (OceanSITES conventions)\")\n",
    "    print(\"  ✓ Required dimensions and variables\")\n",
    "    print(\"  ✓ Variable attributes (units, standard_name, vocabulary)\")\n",
    "    print(\"  ✓ Global attributes (conventions, metadata)\")\n",
    "    print(\"  ✓ Data value ranges (coordinates, valid_min/max)\")\n",
    "    print(\"  ✓ CF convention compliance (dimension ordering)\")\n",
    "    \n",
    "else:\n",
    "    print(\"❌ No AC1 file available for compliance checking\")\n",
    "    print(\"Please ensure the conversion step above succeeded first.\")"
   ]
  }
 ],
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