{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52a57718",
   "metadata": {},
   "source": [
    "# Creating an FVCOM Restart File Using CMEMS Data\n",
    "\n",
    "This tutorial demonstrates how to create an FVCOM restart file using ocean data from the Copernicus Marine Environment Monitoring Service (CMEMS). The process involves reading CMEMS reanalysis data, interpolating it onto the FVCOM grid, and writing it out in the proper restart file format.\n",
    "\n",
    "## Overview\n",
    "\n",
    "FVCOM (Finite Volume Community Ocean Model) restart files contain the initial conditions needed to start or restart a model simulation. These files include variables such as:\n",
    "- Sea surface height (`zeta`)\n",
    "- Temperature (`temp`) \n",
    "- Salinity (`salinity`)\n",
    "- Velocity components (`u`, `v`)\n",
    "\n",
    "CMEMS provides high-quality ocean reanalysis data that can be used to initialize FVCOM models with realistic oceanographic conditions.\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "Before running this tutorial, ensure you have:\n",
    "- Access to CMEMS data files (both 2D and 3D products)\n",
    "- A donor FVCOM restart file (template with correct grid structure)\n",
    "- The PyFVCOM2 package installed\n",
    "\n",
    "## Step 1: Import Required Libraries\n",
    "\n",
    "First, we'll import all the necessary modules from PyFVCOM2 and standard Python libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76244552",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local1/data/scratch/jcl/miniconda/miniconda3/envs/pyfvcom2/lib/python3.11/site-packages/numpy/lib/_format_impl.py:838: VisibleDeprecationWarning: dtype(): align should be passed as Python or NumPy boolean but got `align=0`. Did you mean to pass a tuple to create a subarray type? (Deprecated NumPy 2.4)\n",
      "  array = pickle.load(fp, **pickle_kwargs)\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from pyfvcom2.file_utils import find_file\n",
    "from pyfvcom2.cmems_reader import CMEMSReader\n",
    "from pyfvcom2.fvcom_reader import FVCOMReader\n",
    "from pyfvcom2.interpolation import InterpolationCoordinates, CMEMSInterpolator\n",
    "from pyfvcom2.restart import write_restart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948c41a9",
   "metadata": {},
   "source": [
    "## Step 2: Configure Paths and Parameters\n",
    "\n",
    "Next, we'll set up the file paths and parameters needed for the interpolation process:\n",
    "\n",
    "- `cmems_data_dir`: Root directory containing CMEMS data\n",
    "- `product_name_2d/3d`: Specific CMEMS product names for 2D and 3D variables\n",
    "- `donor_filepath`: Path to an existing FVCOM restart file that provides the target grid\n",
    "- `start_date_time`: The date/time for which we want to create initial conditions\n",
    "- `fvcom_to_cmems_var_names`: Mapping between FVCOM and CMEMS variable names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc13102f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths etc\n",
    "data_dir = os.path.expanduser('~/data/pyfvcom2_doc')\n",
    "cmems_data_dir = f'{data_dir}/CMEMS_NWS_anfc_0.027deg'\n",
    "product_name_2d = 'cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m'\n",
    "product_name_3d = 'cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m'\n",
    "donor_filepath = f'{data_dir}/FVCOM_tamar_estuary/tamar_v2_donor_restart.nc'\n",
    "start_date_time = datetime.strptime(\"20250614\", \"%Y%m%d\")\n",
    "\n",
    "# FVCOM -> CMEMS name map. This is the same as the default name map, but we include it here to demonstrate how it\n",
    "# can be used to define equivalent variables names in FVCOM and CMEMS data.\n",
    "fvcom_to_cmems_var_names = {'temp': 'thetao',\n",
    "                            'salinity': 'so',\n",
    "                            'u': 'uo',\n",
    "                            'v': 'vo',\n",
    "                            'zeta': 'zos'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d57eb4",
   "metadata": {},
   "source": [
    "## Step 3: Set Up Interpolation Coordinates\n",
    "\n",
    "Before interpolating the data, we need to create interpolation coordinate objects that specify where on the FVCOM grid we want the data to be interpolated. Different variables are located at different positions:\n",
    "\n",
    "- **Node variables** (like temperature, salinity, sea surface height): Located at the vertices of the triangular mesh\n",
    "- **Element variables** (like velocity components): Located at the centers of the triangular elements\n",
    "\n",
    "We'll get the spatial coordinates from the FVCOM reader and add the temporal information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cfa2b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accessing FVCOM metadata from: /users/modellers/jcl/data/pyfvcom2_doc/FVCOM_tamar_estuary/tamar_v2_donor_restart.nc\n"
     ]
    }
   ],
   "source": [
    "# FVCOM reader for the restart\n",
    "fvcom_reader = FVCOMReader(donor_filepath)\n",
    "\n",
    "# Interpolation coordinates for variables defined at nodes\n",
    "interp_coords_nodes = fvcom_reader.get_interpolation_coordinates('node', 'layer_centre')\n",
    "interp_coords_nodes.dates = np.asarray([start_date_time])\n",
    "\n",
    "# Interpolation coordinates for variables defined at elements\n",
    "interp_coords_elements = fvcom_reader.get_interpolation_coordinates('element', 'layer_centre')\n",
    "interp_coords_elements.dates = np.asarray([start_date_time])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327bf3e6",
   "metadata": {},
   "source": [
    "## Step 4: Process 2D Variables\n",
    "\n",
    "Now we'll handle the 2D variables (variables that don't vary with depth). In this case, we're processing sea surface height:\n",
    "\n",
    "1. **Find the CMEMS data file**: Use the `find_file` utility to locate the correct file containing data for our target date\n",
    "2. **Create CMEMS reader**: Initialize a reader for the 2D data file (we pass it as a list for consistency with the current API)\n",
    "3. **Create interpolator**: Set up a CMEMS interpolator with variable name mapping\n",
    "4. **Interpolate**: Use the interpolator to map CMEMS data onto the FVCOM grid\n",
    "\n",
    "**Note**: The CMEMSReader can accept either a single file path or a list of file paths. We use a list here to be explicit about the current API design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3949e6fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accessing CMEMS metadata from: /users/modellers/jcl/data/pyfvcom2_doc/CMEMS_NWS_anfc_0.027deg/cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m/cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_20250614_20250614.nc\n",
      "Depth dimension variable name depth not found in CMEMS file /users/modellers/jcl/data/pyfvcom2_doc/CMEMS_NWS_anfc_0.027deg/cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m/cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_20250614_20250614.nc.\n",
      "Assuming the dataset includes 2D variables only.\n",
      "Using dimension variable names:\n",
      "  Time: time\n",
      "  Longitude: longitude\n",
      "  Latitude: latitude\n",
      "Using reference variable zos.\n",
      "Interpolating CMEMS zos to FVCOM grid.\n",
      "Interpolating CMEMS zos to FVCOM grid for date: 2025-06-14 00:00:00.\n"
     ]
    }
   ],
   "source": [
    "# 2D data\n",
    "# -------\n",
    "cmems_data_dir_2d = pathlib.Path(cmems_data_dir, product_name_2d)\n",
    "file_path, time_index = find_file(cmems_data_dir_2d, product_name_2d, start_date_time)\n",
    "\n",
    "# Create reader - pass as list to be explicit about the new API\n",
    "cmems_reader_2d = CMEMSReader([file_path], reference_var_name='zos')\n",
    "\n",
    "# Create interpolator (2D)\n",
    "interpolator = CMEMSInterpolator(cmems_reader_2d, fvcom_to_cmems_var_names)\n",
    "\n",
    "# 2D vars\n",
    "var_data_2d = {}\n",
    "var_data_2d['zeta'] = interpolator.interpolate(interp_coords_nodes, 'zeta')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080e09f0",
   "metadata": {},
   "source": [
    "## Step 5: Process 3D Variables\n",
    "\n",
    "Now we'll handle the 3D variables (variables that vary with depth). These include temperature, salinity, and velocity components:\n",
    "\n",
    "- **Temperature** (`'thetao'` → `'temp'`): Located at model nodes\n",
    "- **Salinity** (`'so'` → `'salinity'`): Located at model nodes  \n",
    "- **U-velocity** (`'uo'` → `'u'`): Located at element centers\n",
    "- **V-velocity** (`'vo'` → `'v'`): Located at element centers\n",
    "\n",
    "The process is similar to 2D variables but uses the 3D CMEMS product and handles multiple variables. We create separate interpolation coordinate objects for node-based and element-based variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b447a91f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accessing CMEMS metadata from: /users/modellers/jcl/data/pyfvcom2_doc/CMEMS_NWS_anfc_0.027deg/cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m/cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_20250614_20250614.nc\n",
      "Using dimension variable names:\n",
      "  Time: time\n",
      "  Depth: depth\n",
      "  Longitude: longitude\n",
      "  Latitude: latitude\n",
      "Using reference variable so.\n",
      "Interpolating CMEMS uo to FVCOM grid.\n",
      "Interpolating CMEMS uo to FVCOM grid for date: 2025-06-14 00:00:00.\n",
      "Interpolating CMEMS vo to FVCOM grid.\n",
      "Interpolating CMEMS vo to FVCOM grid for date: 2025-06-14 00:00:00.\n",
      "Interpolating CMEMS thetao to FVCOM grid.\n",
      "Interpolating CMEMS thetao to FVCOM grid for date: 2025-06-14 00:00:00.\n",
      "Interpolating CMEMS so to FVCOM grid.\n",
      "Interpolating CMEMS so to FVCOM grid for date: 2025-06-14 00:00:00.\n"
     ]
    }
   ],
   "source": [
    "# 3D data \n",
    "# -------     \n",
    "cmems_data_dir_3d = pathlib.Path(cmems_data_dir, product_name_3d)\n",
    "file_path, time_index = find_file(cmems_data_dir_3d, product_name_3d, start_date_time)\n",
    "\n",
    "# Create reader - pass as list to be explicit about the new API\n",
    "cmems_reader_3d = CMEMSReader([file_path], reference_var_name='so')\n",
    "\n",
    "# Create interpolator (3D)\n",
    "interpolator = CMEMSInterpolator(cmems_reader_3d, fvcom_to_cmems_var_names)\n",
    "\n",
    "# 3D vars\n",
    "vars_3d = {'u': 'element',\n",
    "           'v': 'element',\n",
    "           'temp': 'node',\n",
    "           'salinity': 'node'}\n",
    "\n",
    "var_data_3d = {}\n",
    "for var_name, position in vars_3d.items():\n",
    "    if position == 'node':\n",
    "        var_data_3d[var_name] = interpolator.interpolate(interp_coords_nodes, var_name)\n",
    "    else: # 'element'\n",
    "        var_data_3d[var_name] = interpolator.interpolate(interp_coords_elements, var_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652bd1f9",
   "metadata": {},
   "source": [
    "## Step 6: Combine Results\n",
    "\n",
    "We'll combine the 2D and 3D variable dictionaries to create our complete dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3feb6d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine data\n",
    "var_data = var_data_2d | var_data_3d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99a2931",
   "metadata": {},
   "source": [
    "## Step 7: Write the Restart File\n",
    "\n",
    "Now we'll create the actual FVCOM restart file using the interpolated data. The `write_restart` function takes:\n",
    "\n",
    "- The donor file path (provides the grid structure and metadata)\n",
    "- The output file path \n",
    "- The dictionary of interpolated variables\n",
    "- The start date/time for the restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de0f2049",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the restart\n",
    "# ------------------\n",
    "\n",
    "# Create data directory for the restart\n",
    "restart_dir = pathlib.Path('data/restart')\n",
    "restart_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "restart_file = pathlib.Path(f'{restart_dir}', 'tamar_cmems_restart_test.nc')\n",
    "write_restart(donor_filepath, restart_file, var_data, start_date_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7534e84",
   "metadata": {},
   "source": [
    "## Step 8: Visualize the Results\n",
    "\n",
    "Finally, let's create a simple visualization to verify that our restart file was created correctly. We'll plot the surface temperature field using PyFVCOM2's plotting capabilities.\n",
    "\n",
    "**Note**: FVCOM restart files can have different dimension ordering depending on the version and configuration. We'll check the array dimensions and handle both common cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bed698b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the result\n",
    "# ---------------\n",
    "from matplotlib import pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from netCDF4 import Dataset\n",
    "\n",
    "from pyfvcom2.plotting import FVCOMPlotter, create_figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de013a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature array shape: (1, 24, 39910)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 393.701x393.701 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an FVCOM plotter for our restart file\n",
    "plotter = FVCOMPlotter(restart_file)\n",
    "\n",
    "# Create a figure with geographic projection\n",
    "fig, ax = create_figure(projection=ccrs.PlateCarree())\n",
    "\n",
    "# Open the restart file and extract surface temperature\n",
    "with Dataset(restart_file) as ds:\n",
    "    temp = ds.variables['temp'][:]\n",
    "    print(f\"Temperature array shape: {temp.shape}\")\n",
    "    \n",
    "    # Plot the surface temperature \n",
    "    # For restart files, dimensions are typically (time, siglay, node) or (siglay, node)\n",
    "    if temp.ndim == 3:\n",
    "        # (time, siglay, node) - use time=0, surface layer (last siglay index)\n",
    "        temp_surface = temp[0, -1, :]\n",
    "    elif temp.ndim == 2:\n",
    "        # (siglay, node) - use surface layer (last siglay index)  \n",
    "        temp_surface = temp[-1, :]\n",
    "    else:\n",
    "        # Fallback - use as is\n",
    "        temp_surface = temp\n",
    "\n",
    "plotter.plot_field(ax, temp_surface, add_colour_bar=True, cb_label='Temperature (°C)')\n",
    "ax.set_title('Surface Temperature from FVCOM Restart File')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b6fd23",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Congratulations! You have successfully created an FVCOM restart file using CMEMS reanalysis data. Here's what we accomplished:\n",
    "\n",
    "1. **Data Integration**: Combined high-quality oceanographic data from CMEMS with FVCOM's unstructured grid format\n",
    "2. **Spatial Interpolation**: Converted data from CMEMS's regular grid to FVCOM's triangular mesh using the `CMEMSInterpolator` class\n",
    "3. **Variable Mapping**: Correctly mapped between different variable naming conventions using the `fvcom_to_cmems_var_names` dictionary\n",
    "4. **File Creation**: Generated a properly formatted NetCDF restart file ready for use in FVCOM\n",
    "\n",
    "## Key Components of the PyFVCOM2 API\n",
    "\n",
    "This tutorial demonstrates several important PyFVCOM2 components:\n",
    "\n",
    "- **CMEMSReader**: Handles reading CMEMS NetCDF data files with flexible file input (single file or lists)\n",
    "- **InterpolationCoordinates**: Unified coordinate handling for different grid positions (nodes vs elements)\n",
    "- **CMEMSInterpolator**: Dedicated interpolator class for CMEMS data with automatic 2D/3D handling and variable name mapping\n",
    "- **FVCOMReader**: Provides grid information and coordinate extraction from FVCOM files\n",
    "- **write_restart**: Creates properly formatted FVCOM restart files\n",
    "\n",
    "## API Notes\n",
    "\n",
    "- The **CMEMSReader** accepts either a single file path or a list of file paths for flexibility\n",
    "- **Interpolation coordinates** are obtained from the FVCOM grid and extended with time information\n",
    "- **Variable name mapping** allows seamless translation between CMEMS and FVCOM naming conventions\n",
    "- **Grid positioning** is handled automatically - variables are interpolated to the correct locations (nodes or elements)\n",
    "\n",
    "This approach provides excellent separation of concerns and flexibility for different data sources and interpolation methods while maintaining ease of use for common workflows."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}