{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6a29764-f39c-431c-8e77-fbc6bfe20f01",
   "metadata": {},
   "source": [
    "# Demo: mooring-level processing (Step 1)\n",
    "\n",
    "This notebook demonstrates the first of the mooring-level processing steps, time_gridding.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006edeaa",
   "metadata": {},
   "source": [
    "## Step 1: Time Gridding and Optional Filtering Demo\n",
    "\n",
    "This notebook demonstrates the Step 1 processing workflow for mooring data:\n",
    "- Loading multiple instrument datasets\n",
    "- Optional time-domain filtering (applied BEFORE interpolation)\n",
    "- Interpolating onto a common time grid\n",
    "- Combining into a unified mooring dataset\n",
    "\n",
    "**Key Point**: Filtering is applied to individual instrument records on their native time grids BEFORE interpolation to preserve data integrity.\n",
    "\n",
    "Version: 1.0  \n",
    "Date: 2025-09-07"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a1920f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:25.881842Z",
     "iopub.status.busy": "2025-09-25T06:33:25.881669Z",
     "iopub.status.idle": "2025-09-25T06:33:26.476041Z",
     "shell.execute_reply": "2025-09-25T06:33:26.475467Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import yaml\n",
    "\n",
    "# Import the time gridding module\n",
    "from oceanarray.time_gridding import (\n",
    "    TimeGriddingProcessor,\n",
    "    time_gridding_mooring,\n",
    "    process_multiple_moorings_time_gridding\n",
    ")\n",
    "\n",
    "# Set up plotting\n",
    "plt.style.use('default')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad6e0e5",
   "metadata": {
    "vscode": {
     "languageId": "javascript"
    }
   },
   "source": [
    "### Configuration\n",
    "\n",
    "First, let's set up our data paths and examine the mooring configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce860d75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:26.478114Z",
     "iopub.status.busy": "2025-09-25T06:33:26.477834Z",
     "iopub.status.idle": "2025-09-25T06:33:26.481436Z",
     "shell.execute_reply": "2025-09-25T06:33:26.481011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing directory: ../data/moor/proc/dsE_1_2018\n",
      "Configuration file: ../data/moor/proc/dsE_1_2018/dsE_1_2018.mooring.yaml\n",
      "Config exists: True\n"
     ]
    }
   ],
   "source": [
    "# Set your data paths here\n",
    "basedir = '../data'\n",
    "mooring_name = 'dsE_1_2018'\n",
    "\n",
    "# Construct paths\n",
    "proc_dir = Path(basedir) / 'moor' / 'proc' / mooring_name\n",
    "config_file = proc_dir / f\"{mooring_name}.mooring.yaml\"\n",
    "\n",
    "print(f\"Processing directory: {proc_dir}\")\n",
    "print(f\"Configuration file: {config_file}\")\n",
    "print(f\"Config exists: {config_file.exists()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4b5b029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:26.482986Z",
     "iopub.status.busy": "2025-09-25T06:33:26.482821Z",
     "iopub.status.idle": "2025-09-25T06:33:26.497410Z",
     "shell.execute_reply": "2025-09-25T06:33:26.496988Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mooring Configuration:\n",
      "Name: dsE_1_2018\n",
      "Water depth: 929 m\n",
      "Location: 65.47561666666667°N, -29.570083333333333°E\n",
      "\n",
      "Instruments (23):\n",
      "  1. sbe56 (serial: unknown) at 629 m\n",
      "  2. sbe16 (serial: unknown) at 679 m\n",
      "  3. sbe56 (serial: unknown) at 689 m\n",
      "  4. sbe56 (serial: unknown) at 699 m\n",
      "  5. sbe56 (serial: unknown) at 709 m\n",
      "  6. sbe56 (serial: unknown) at 719 m\n",
      "  7. sbe56 (serial: unknown) at 729 m\n",
      "  8. sbe56 (serial: unknown) at 739 m\n",
      "  9. sbe56 (serial: unknown) at 749 m\n",
      "  10. sbe56 (serial: unknown) at 759 m\n",
      "  11. sbe56 (serial: unknown) at 769 m\n",
      "  12. sbe16 (serial: unknown) at 780 m\n",
      "  13. tr1050 (serial: unknown) at 790 m\n",
      "  14. rbrsolo (serial: unknown) at 800 m\n",
      "  15. tr1050 (serial: unknown) at 810 m\n",
      "  16. rbrsolo (serial: unknown) at 820 m\n",
      "  17. tr1050 (serial: unknown) at 830 m\n",
      "  18. rbrsolo (serial: unknown) at 840 m\n",
      "  19. tr1050 (serial: unknown) at 850 m\n",
      "  20. rbrsolo (serial: unknown) at 860 m\n",
      "  21. tr1050 (serial: unknown) at 870 m\n",
      "  22. microcat (serial: unknown) at 880 m\n",
      "  23. sbe56 (serial: unknown) at 905 m\n"
     ]
    }
   ],
   "source": [
    "# Load and examine the mooring configuration\n",
    "if config_file.exists():\n",
    "    with open(config_file, 'r') as f:\n",
    "        config = yaml.safe_load(f)\n",
    "\n",
    "    print(\"Mooring Configuration:\")\n",
    "    print(f\"Name: {config['name']}\")\n",
    "    print(f\"Water depth: {config.get('waterdepth', 'unknown')} m\")\n",
    "    print(f\"Location: {config.get('latitude', 'unknown')}°N, {config.get('longitude', 'unknown')}°E\")\n",
    "    print(f\"\\nInstruments ({len(config.get('instruments', []))}):\")\n",
    "\n",
    "    for i, inst in enumerate(config.get('instruments', [])):\n",
    "        print(f\"  {i+1}. {inst.get('instrument', 'unknown')} \"\n",
    "              f\"(serial: {inst.get('serial num.', 'unknown')}) at {inst.get('depth', 'unknown')} m\")\n",
    "else:\n",
    "    print(\"Configuration file not found!\")\n",
    "    print(\"Please check your data path and mooring name.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87567a6",
   "metadata": {
    "vscode": {
     "languageId": "javascript"
    }
   },
   "source": [
    "### Examine individual instrument files\n",
    "\n",
    "Let's look at the individual instrument files before processing to understand the different sampling rates and data characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b824bf8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:26.499111Z",
     "iopub.status.busy": "2025-09-25T06:33:26.498935Z",
     "iopub.status.idle": "2025-09-25T06:33:26.544229Z",
     "shell.execute_reply": "2025-09-25T06:33:26.543758Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Instrument   |   Serial |   Depth [m] | File                   | Start               | End                 |   Points | Sampling   | Variables                                                                                                                |\n",
      "|:-------------|---------:|------------:|:-----------------------|:--------------------|:--------------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|\n",
      "| sbe56        |     6363 |         629 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe16        |     2419 |         679 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6401 |         689 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6402 |         699 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     8482 |         709 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6365 |         719 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6409 |         729 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6397 |         739 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6366 |         749 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6394 |         759 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe56        |     6370 |         769 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| sbe16        |     2418 |         780 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| tr1050       |    13889 |         790 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| rbrsolo      |   101651 |         800 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| tr1050       |    15580 |         810 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| rbrsolo      |   101647 |         820 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| tr1050       |    13874 |         830 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| rbrsolo      |   101645 |         840 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| tr1050       |    15574 |         850 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| rbrsolo      |   101646 |         860 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| tr1050       |    15577 |         870 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "| microcat     |     7518 |         880 | dsE_1_2018_7518_use.nc | 2018-08-13T07:25:51 | 2018-08-26T10:37:50 |   113473 | 10.0 sec   | temperature, salinity, conductivity, pressure, serial_number, InstrDepth, instrument, clock_offset, start_time, end_time |\n",
      "| sbe56        |     6364 |         905 | MISSING                |                     |                     |        0 |            |                                                                                                                          |\n",
      "\n",
      "Found 1 instrument datasets\n"
     ]
    }
   ],
   "source": [
    "# Find and examine individual instrument files\n",
    "file_suffix = \"_use\"\n",
    "instrument_files = []\n",
    "instrument_datasets = []\n",
    "rows = []\n",
    "\n",
    "if config_file.exists():\n",
    "    for inst_config in config.get(\"instruments\", []):\n",
    "        instrument_type = inst_config.get(\"instrument\", \"unknown\")\n",
    "        serial = inst_config.get(\"serial\", 0)\n",
    "        depth = inst_config.get(\"depth\", 0)\n",
    "\n",
    "        # Look for the file\n",
    "        filename = f\"{mooring_name}_{serial}{file_suffix}.nc\"\n",
    "        filepath = proc_dir / instrument_type / filename\n",
    "\n",
    "        if filepath.exists():\n",
    "            ds = xr.open_dataset(filepath)\n",
    "            instrument_files.append(filepath)\n",
    "            instrument_datasets.append(ds)\n",
    "\n",
    "            # Time coverage\n",
    "            t0, t1 = ds.time.values[0], ds.time.values[-1]\n",
    "            npoints = len(ds.time)\n",
    "\n",
    "            # Median sampling interval\n",
    "            time_diff = np.diff(ds.time.values) / np.timedelta64(1, \"m\")  # in minutes\n",
    "            median_interval = np.nanmedian(time_diff)\n",
    "            if median_interval > 1:\n",
    "                sampling = f\"{median_interval:.1f} min\"\n",
    "            else:\n",
    "                sampling = f\"{median_interval*60:.1f} sec\"\n",
    "\n",
    "            # Collect a row for the table\n",
    "            rows.append(\n",
    "                {\n",
    "                    \"Instrument\": instrument_type,\n",
    "                    \"Serial\": serial,\n",
    "                    \"Depth [m]\": depth,\n",
    "                    \"File\": filepath.name,\n",
    "                    \"Start\": str(t0)[:19],\n",
    "                    \"End\": str(t1)[:19],\n",
    "                    \"Points\": npoints,\n",
    "                    \"Sampling\": sampling,\n",
    "                    \"Variables\": \", \".join(list(ds.data_vars)),\n",
    "                }\n",
    "            )\n",
    "        else:\n",
    "            rows.append(\n",
    "                {\n",
    "                    \"Instrument\": instrument_type,\n",
    "                    \"Serial\": serial,\n",
    "                    \"Depth [m]\": depth,\n",
    "                    \"File\": \"MISSING\",\n",
    "                    \"Start\": \"\",\n",
    "                    \"End\": \"\",\n",
    "                    \"Points\": 0,\n",
    "                    \"Sampling\": \"\",\n",
    "                    \"Variables\": \"\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "    # Make a DataFrame summary\n",
    "    summary = pd.DataFrame(rows)\n",
    "    pd.set_option(\"display.max_colwidth\", 80)  # allow long var lists\n",
    "    print(summary.to_markdown(index=False))\n",
    "\n",
    "    print(f\"\\nFound {len(instrument_datasets)} instrument datasets\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd5ace6",
   "metadata": {},
   "source": [
    "### Process with time gridding (no filtering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9430b5f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:26.545935Z",
     "iopub.status.busy": "2025-09-25T06:33:26.545777Z",
     "iopub.status.idle": "2025-09-25T06:33:26.832403Z",
     "shell.execute_reply": "2025-09-25T06:33:26.831901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing mooring with time gridding only (no filtering)...\n",
      "============================================================\n",
      "Starting Step 1 (time gridding) processing for mooring: dsE_1_2018\n",
      "Using files with suffix: _use\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6363_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe16/dsE_1_2018_2419_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6401_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6402_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_8482_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6365_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6409_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6397_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6366_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6394_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6370_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe16/dsE_1_2018_2418_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/tr1050/dsE_1_2018_13889_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/rbrsolo/dsE_1_2018_101651_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/tr1050/dsE_1_2018_15580_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/rbrsolo/dsE_1_2018_101647_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/tr1050/dsE_1_2018_13874_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/rbrsolo/dsE_1_2018_101645_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/tr1050/dsE_1_2018_15574_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/rbrsolo/dsE_1_2018_101646_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/tr1050/dsE_1_2018_15577_use.nc\n",
      "Loading microcat serial 7518: dsE_1_2018_7518_use.nc\n",
      "WARNING: File not found: ../data/moor/proc/dsE_1_2018/sbe56/dsE_1_2018_6364_use.nc\n",
      "\n",
      "WARNING: Missing instruments compared to YAML configuration:\n",
      "   - sbe56:6363\n",
      "   - sbe16:2419\n",
      "   - sbe56:6401\n",
      "   - sbe56:6402\n",
      "   - sbe56:8482\n",
      "   - sbe56:6365\n",
      "   - sbe56:6409\n",
      "   - sbe56:6397\n",
      "   - sbe56:6366\n",
      "   - sbe56:6394\n",
      "   - sbe56:6370\n",
      "   - sbe16:2418\n",
      "   - tr1050:13889\n",
      "   - rbrsolo:101651\n",
      "   - tr1050:15580\n",
      "   - rbrsolo:101647\n",
      "   - tr1050:13874\n",
      "   - rbrsolo:101645\n",
      "   - tr1050:15574\n",
      "   - rbrsolo:101646\n",
      "   - tr1050:15577\n",
      "   - sbe56:6364\n",
      "   Expected 23, found 1\n",
      "\n",
      "Loaded 1 instrument datasets\n",
      "Dataset 0 depth 880 [microcat:7518]:\n",
      "  Start: 2018-08-13T07:25:51, End: 2018-08-26T10:37:50\n",
      "  Time interval - Median: 10.02 sec, Range: 9.94 sec to 10.02 sec, Std: 0.04 sec\n",
      "  Variables: ['temperature', 'salinity', 'conductivity', 'pressure', 'serial_number', 'InstrDepth', 'instrument', 'clock_offset', 'start_time', 'end_time']\n",
      "\n",
      "TIMING ANALYSIS:\n",
      "   Overall median interval: 0.17 min\n",
      "   Range of median intervals: 0.17 to 0.17 min\n",
      "   Common grid will use 0.17 min intervals\n",
      "       microcat:7518: 0.17 min -> 0.17 min (0.0% change) MINIMAL CHANGE\n",
      "   Common time grid: 113472 points from 2018-08-13T07:25:51.008000000 to 2018-08-26T10:37:41.008000000\n",
      "\n",
      "INTERPOLATING FILTERED DATASETS ONTO COMMON GRID:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully interpolated dataset 0\n",
      "Variable 'u_velocity' not found in any dataset, skipping\n",
      "Variable 'v_velocity' not found in any dataset, skipping\n",
      "Successfully wrote time-gridded dataset: ../data/moor/proc/dsE_1_2018/dsE_1_2018_mooring_use.nc\n",
      "Combined dataset shape: {'time': 113472, 'N_LEVELS': 1}\n",
      "Variables: ['temperature', 'salinity', 'conductivity', 'pressure', 'instrument_id']\n",
      "\n",
      "Processing result: SUCCESS\n"
     ]
    }
   ],
   "source": [
    "# Process without filtering\n",
    "print(\"Processing mooring with time gridding only (no filtering)...\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "result = time_gridding_mooring(mooring_name, basedir, file_suffix='_use')\n",
    "\n",
    "print(f\"\\nProcessing result: {'SUCCESS' if result else 'FAILED'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60fdf8e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:26.834121Z",
     "iopub.status.busy": "2025-09-25T06:33:26.833869Z",
     "iopub.status.idle": "2025-09-25T06:33:26.846062Z",
     "shell.execute_reply": "2025-09-25T06:33:26.845589Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output file exists: ../data/moor/proc/dsE_1_2018/dsE_1_2018_mooring_use.nc\n"
     ]
    }
   ],
   "source": [
    "# Load and examine the combined dataset\n",
    "output_file = proc_dir / f\"{mooring_name}_mooring_use.nc\"\n",
    "\n",
    "if output_file.exists():\n",
    "    print(f\"Output file exists: {output_file}\")\n",
    "\n",
    "    # Load the combined dataset\n",
    "    combined_ds = xr.open_dataset(output_file)\n",
    "else:\n",
    "    print(\"Output file not found - processing may have failed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab26f061",
   "metadata": {},
   "source": [
    "### Visualize Combined Dataset\n",
    "\n",
    "Let's plot the combined dataset to see how the different instruments look on the common time grid.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68780c36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-25T06:33:26.847775Z",
     "iopub.status.busy": "2025-09-25T06:33:26.847588Z",
     "iopub.status.idle": "2025-09-25T06:33:27.645981Z",
     "shell.execute_reply": "2025-09-25T06:33:27.645446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_combined_timeseries(\n",
    "    combined_ds,\n",
    "    variables=(\"temperature\", \"salinity\", \"pressure\"),\n",
    "    cmap_name=\"viridis\",\n",
    "    line_alpha=0.8,\n",
    "    line_width=1.2,\n",
    "    percentile_limits=(1, 99),\n",
    "):\n",
    "    \"\"\"\n",
    "    Plot selected variables from a combined mooring dataset as stacked time series.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    combined_ds : xarray.Dataset\n",
    "        Must have dims: time, N_LEVELS. Optional coords: nominal_depth, serial_number.\n",
    "    variables : iterable[str]\n",
    "        Variable names to try to plot (if present in dataset).\n",
    "    cmap_name : str\n",
    "        Matplotlib colormap name for coloring by instrument level.\n",
    "    line_alpha : float\n",
    "        Line transparency.\n",
    "    line_width : float\n",
    "        Line width.\n",
    "    percentile_limits : (low, high)\n",
    "        Percentiles to use for automatic y-limits (e.g., (1, 99)).\n",
    "    \"\"\"\n",
    "    if combined_ds is None:\n",
    "        print(\"Combined dataset not available.\")\n",
    "        return None, None\n",
    "    n_levels = combined_ds.sizes.get(\"N_LEVELS\")\n",
    "    if n_levels is None:\n",
    "        raise ValueError(\"Dataset must contain dimension 'N_LEVELS'.\")\n",
    "\n",
    "    available = [v for v in variables if v in combined_ds.data_vars]\n",
    "    if not available:\n",
    "        print(\"No requested variables found to plot.\")\n",
    "        return None, None\n",
    "\n",
    "    # Colors by level\n",
    "    cmap = plt.get_cmap(cmap_name)\n",
    "    colors = cmap(np.linspace(0, 1, n_levels))\n",
    "\n",
    "    fig, axes = plt.subplots(\n",
    "        len(available), 1, figsize=(14, 3.6 * len(available)), sharex=True, constrained_layout=True\n",
    "    )\n",
    "    if len(available) == 1:\n",
    "        axes = [axes]\n",
    "\n",
    "    depth_arr = combined_ds.get(\"nominal_depth\")\n",
    "    serial_arr = combined_ds.get(\"serial_number\")\n",
    "\n",
    "    first_axis = True\n",
    "    for ax, var in zip(axes, available):\n",
    "        values_for_limits = []\n",
    "        for level in range(n_levels):\n",
    "            depth = None if depth_arr is None else depth_arr.values[level]\n",
    "            serial = None if serial_arr is None else serial_arr.values[level]\n",
    "            label = None\n",
    "            if first_axis:\n",
    "                if depth is not None and np.isfinite(depth):\n",
    "                    label = f\"Serial {serial} ({int(depth)} m)\" if serial is not None else f\"({int(depth)} m)\"\n",
    "                elif serial is not None:\n",
    "                    label = f\"Serial {serial}\"\n",
    "\n",
    "            da = combined_ds[var].isel(N_LEVELS=level)\n",
    "            da = da.where(np.isfinite(da), drop=True)\n",
    "            if da.size == 0:\n",
    "                continue\n",
    "\n",
    "            values_for_limits.append(da.values)\n",
    "\n",
    "            ax.plot(\n",
    "                da[\"time\"].values,\n",
    "                da.values,\n",
    "                color=colors[level],\n",
    "                alpha=line_alpha,\n",
    "                linewidth=line_width,\n",
    "                label=label,\n",
    "            )\n",
    "\n",
    "        # Set labels and grid\n",
    "        ax.set_ylabel(var.replace(\"_\", \" \").title())\n",
    "        ax.grid(True, alpha=0.3)\n",
    "        ax.set_title(f\"{var.replace('_', ' ').title()} — Combined Time Grid\")\n",
    "\n",
    "        # Legend only once\n",
    "        if first_axis:\n",
    "            ax.legend(ncol=3, fontsize=8, loc=\"upper right\", frameon=False)\n",
    "            first_axis = False\n",
    "\n",
    "        # Auto y-limits based on percentiles\n",
    "        if values_for_limits:\n",
    "            flat = np.concatenate(values_for_limits)\n",
    "            low, high = np.nanpercentile(flat, percentile_limits)\n",
    "            ax.set_ylim(low, high)\n",
    "\n",
    "    axes[-1].set_xlabel(\"Time\")\n",
    "    return fig, axes\n",
    "\n",
    "# Usage:\n",
    "if 'combined_ds' in locals():\n",
    "    plot_combined_timeseries(combined_ds)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv (3.11.7)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
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 },
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}