{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# SCHISM procedural example\n",
    "\n",
    "In this notebook we will use the SCHOSM grid, config and data objects to define a SCHISM workspace"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frontmatter\n",
    "Required inputs and defination of a few helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# turn off warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "import sys\n",
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "from rompy.core import DataBlob, TimeRange\n",
    "from shutil import rmtree\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import intake\n",
    "import cartopy.crs as ccrs\n",
    "import pandas as pd \n",
    "\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "HERE = Path('./rompy/tests/schism')\n",
    "\n",
    "\n",
    "\n",
    "# Define some useful functions for plotting outputs\n",
    "\n",
    "from matplotlib.tri import Triangulation\n",
    "import cartopy.feature as cfeature\n",
    "#import cartopy.mpl.ticker as cticker\n",
    "from cartopy.io.shapereader import Reader\n",
    "from cartopy.feature import ShapelyFeature\n",
    "\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "lon_formatter = LongitudeFormatter()\n",
    "lat_formatter = LatitudeFormatter()\n",
    "\n",
    "import re\n",
    "from pyproj import Proj, transform\n",
    "import pytz\n",
    "\n",
    "from scipy.spatial import KDTree\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "def schism_load(schfile):\n",
    "    ''' \n",
    "    Ron: Load schism output file and parse the element/node values to create a (matplotlib) trimesh object for plotting\n",
    "    \n",
    "    Returns xarray dataset and trimesh object\n",
    "    '''\n",
    "    schout = xr.open_dataset(schfile)\n",
    "    elems = np.int32(schout.SCHISM_hgrid_face_nodes[:,:-1]-1)\n",
    "    # Ron: get lat/lon coordinates of nodes - weird it appears x,y are switched \n",
    "    lons = schout.SCHISM_hgrid_node_y.values\n",
    "    lats = schout.SCHISM_hgrid_node_x.values\n",
    "    # create trimesh object\n",
    "    meshtri = Triangulation(lats, lons, triangles=elems)\n",
    "    return schout, meshtri\n",
    "\n",
    "def schism_plot(schout, meshtri,varname,varscale=[],bbox=[],time=-1,mask=True,\n",
    "                vectors=False, plotmesh=False,project=False,contours=[10,30,50], \n",
    "                pscale=20,cmap=plt.cm.jet):\n",
    "    ''' \n",
    "    plot output variable in xarray dataset (schout) using mesh information meshtri.\n",
    "    input:\n",
    "        schout: xarray dataset returned by def schism_load\n",
    "        meshtri: (matplotlib) trimesh object returned by def schism_load\n",
    "        varname: name of data variable in schout\n",
    "        OPTIONAL:\n",
    "            varscale: min/max plotting colour values (defalts to min/max)\n",
    "            bbox: bounding box for plotting [minlon,minlat,maxlon,maxlat] (defalts to data bounds)\n",
    "            time: time to plot (if a variable dimension), can be int (index) or datetime64-like object\n",
    "            plotmesh: plot the grid mesh (elemment boundaries)\n",
    "            project: use a map projection (cartopy, so that e.g. gis data can be added - this is slower\n",
    "            mask: mask out parts of the SCHISM output based on a minumum depth threshold\n",
    "    Returns xarray dataset and \n",
    "    We should modify this to load multiple files ... probably need assistance from DASK\n",
    "    '''\n",
    "    if 'time' in list(schout.dims):\n",
    "        if type(time)==int : # input ts is index\n",
    "            schout=schout.isel(time=time)\n",
    "        else: # assume is datetime64-like object\n",
    "            schout=schout.sel(time=time)\n",
    "    # By default, plot depth contours \n",
    "    # ... I like depth to be z (negative)\n",
    "    z=schout.depth*-1\n",
    "    if np.mean(contours)>0:\n",
    "        contours = np.flip(-1*np.asarray(contours))\n",
    "    else:\n",
    "        contours = np.asarray(contours)\n",
    "    if varname=='depth' or varname=='z':\n",
    "        var = z\n",
    "    else:\n",
    "        var=schout[varname]\n",
    "    \n",
    "    if len(varscale)==0: \n",
    "        vmin=var.min()\n",
    "        vmax=var.max()\n",
    "    else:\n",
    "        vmin,vmax=varscale\n",
    "    if project:\n",
    "        x,y = meshtri.x,meshtri.y\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(pscale,pscale),\n",
    "            subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "        if len(bbox)==4:\n",
    "            ax.set_extent([bbox[0], bbox[2], bbox[1], bbox[3]], ccrs.PlateCarree())\n",
    "        else:\n",
    "            bbox=[x.min(),y.min(),x.max(),y.max()]\n",
    "    else:\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(30,30))\n",
    "    if plotmesh:\n",
    "        ax.triplot(meshtri, color='k', alpha=0.3)\n",
    "    ### MASKING ** doesnt work with tripcolor, must use tricontouf ###############################\n",
    "    # mask all places in the grid where depth is greater than elev (i.e. are \"dry\") by threshold below\n",
    "    if mask:\n",
    "        # temp=var.values\n",
    "        # threshold of + 0.05 seems pretty good   *** But do we want to use the minimum depth\n",
    "        # defined in the SCHISM input (H0) and in output schout.minimum_depth\n",
    "        # bad_idx= schout.elev.values+schout.depth.values<0.05\n",
    "        bad_idx= schout.elev.values+schout.depth.values < schout.minimum_depth.values\n",
    "        # new way\n",
    "        mask = np.all(np.where(bad_idx[meshtri.triangles], True, False), axis=1)\n",
    "        meshtri.set_mask(mask)\n",
    "        \n",
    "        extend='neither'\n",
    "        if (var.min()<vmin) & (var.max()>vmax):\n",
    "            extend='both'\n",
    "        elif var.min()<vmin:\n",
    "            extend='min'\n",
    "        elif var.max()>vmax:\n",
    "            extend='max'\n",
    "\n",
    "        cax = ax.tricontourf(meshtri, var, cmap=cmap,levels=np.linspace(vmin, vmax, 50), extend=extend)\n",
    "    #no mask#############################################################\n",
    "    else:\n",
    "        cax = ax.tripcolor(meshtri, var, cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "        # quiver variables if asked\n",
    "    if vectors:\n",
    "        if re.search('WWM',varname):\n",
    "            vtype='waves'\n",
    "        else:\n",
    "            vtype='currents'\n",
    "        LonI,LatI,UI,VI=schism_calculate_vectors(ax, schout, vtype=vtype)\n",
    "        ax.quiver(LonI,LatI,UI,VI, color='k')\n",
    "\n",
    "    con = ax.tricontour(meshtri, z, contours, colors='k')\n",
    "    # ax.clabel(con, con.levels, inline=True, fmt='%i', fontsize=12)\n",
    "    if not(project):\n",
    "        ax.set_aspect('equal')\n",
    "    else:\n",
    "        # draw lat/lon grid lines every n degrees.\n",
    "        #  n = (max(lat)-min(lat))/8\n",
    "        n = (bbox[2]-bbox[0])/5\n",
    "        for fac in [1,10,100]:\n",
    "            nr = np.round(n*fac)/fac\n",
    "            if nr>0:\n",
    "                n=nr\n",
    "                xticks = np.arange(np.round(bbox[0]*fac)/fac,np.round(bbox[2]*fac)/fac+n,n)\n",
    "                yticks = np.arange(np.round(bbox[1]*fac)/fac,np.round(bbox[3]*fac)/fac+n,n)\n",
    "                break\n",
    "#        ax.set_xticks(xticks, crs=ccrs.PlateCarree()\n",
    "        ax.set_xticks(xticks)\n",
    "#         ax.set_xticklabels(np.arange(np.round(min(x)),np.round(max(x)),n))\n",
    "#        ax.set_yticks(yticks, crs=ccrs.PlateCarree()\n",
    "        ax.set_yticks(yticks)\n",
    "#         ax.set_yticklabels(np.arange(np.round(min(y)),np.round(max(y)),n))\n",
    "        ax.yaxis.tick_left()\n",
    "        #lon_formatter = cticker.LongitudeFormatter()\n",
    "        #lat_formatter = cticker.LatitudeFormatter()\n",
    "        # New versions of marplotlib throw warnings on this - does it matter\n",
    "\t# ax.xaxis.set_major_formatter(lon_formatter)\n",
    "        # ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\t#ax.set_xticks(lon_formatter)\n",
    "\t#ax.set_yticks(lat_formatter)\n",
    "        ax.grid(linewidth=1, color='black', alpha=0.5, linestyle='--')\n",
    "        ax.add_feature(cfeature.BORDERS, linewidth=2)\n",
    "    if len(bbox)==4:\n",
    "        ax.set_ylim(bbox[1], bbox[3])\n",
    "        ax.set_xlim(bbox[0], bbox[2])\n",
    "    cbar = plt.colorbar(mappable=cax,shrink=0.5)\n",
    "    cbar.set_ticks(np.round(np.linspace(vmin,vmax,5)*100)/100) \n",
    "    cbar.set_label(varname)\n",
    "    \n",
    "    return ax\n",
    "\n",
    "#Nautical convention\n",
    "def pol2cart2(rho, deg):\n",
    "    x, y = pol2cart(rho, deg/180.*np.pi)\n",
    "    return y, x\n",
    "\n",
    "# Cartesian convention\n",
    "def pol2cart(rho, phi):\n",
    "    x = rho * np.cos(phi)\n",
    "    y = rho * np.sin(phi)\n",
    "    return x, y\n",
    "\n",
    "def schism_calculate_vectors(ax, schout, vtype='waves', dX='auto', mask=True):\n",
    "    pUTM55 = Proj('epsg:32755')\n",
    "    # pWGS84 = Proj('epsg:4326')\n",
    "    if vtype=='waves':\n",
    "        idx=(schout.WWM_1>0.05) & (schout.elev-schout.depth<0.1)\n",
    "        dp=schout.WWM_18[idx]\n",
    "        # hs=schout.WWM_1[idx]\n",
    "        hs=np.ones(dp.shape)\n",
    "        [u,v] = pol2cart2(hs, np.mod(dp+180, 360))\n",
    "    elif vtype=='elev'or re.search('curr',vtype):\n",
    "        idx=np.sqrt(schout.dahv[:,0]**2+schout.dahv[:,1]**2)>0.01\n",
    "        u = schout.dahv[idx,0] \n",
    "        v = schout.dahv[idx,1] #dahv has u and v components, so use index of 1 for v and index of 0 for u\n",
    "    else: \n",
    "        print('*** Warning input vecter data not understood')\n",
    "    x,y = pUTM55(schout.SCHISM_hgrid_node_x.values[idx],schout.SCHISM_hgrid_node_y.values[idx])\n",
    "    xlim,ylim=pUTM55(ax.get_xlim(),ax.get_ylim())\n",
    "    # might have to play with this - we assume a total of 100 arrows a side will be pleasing\n",
    "    if dX=='auto':\n",
    "        n=30\n",
    "        dX = np.ceil((ylim[1]-ylim[0])/n)\n",
    "    xi = np.arange(xlim[0],xlim[1]+dX,dX)\n",
    "    yi = np.arange(ylim[0],ylim[1]+dX,dX)\n",
    "    XI,YI = np.meshgrid(xi,yi)\n",
    "\n",
    "    UI = griddata((x,y),u,(XI,YI),method='linear')\n",
    "    VI = griddata((x,y),v,(XI,YI),method='linear')\n",
    "    # Create a mask so that place with very little data are removed\n",
    "    if mask:\n",
    "        xbins = np.arange(xlim[0],xlim[1]+2*dX,dX)\n",
    "        ybins = np.arange(ylim[0],ylim[1]+2*dX,dX)\n",
    "        densityH,_,_ = np.histogram2d(x, y, bins=[xbins,ybins])\n",
    "        densityH=densityH.T\n",
    "        # might want to adjust this...\n",
    "        idx=densityH<1\n",
    "        UI[idx]=np.NaN\n",
    "        VI[idx]=np.NaN\n",
    "    LonI,LatI = pUTM55(XI,YI, inverse=True)\n",
    "\n",
    "    return LonI,LatI,UI,VI"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Workspace basepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir = Path(\"schism_procedural\")\n",
    "if workdir.exists():\n",
    "    rmtree(workdir)\n",
    "\n",
    "workdir.mkdir(exist_ok=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('rompy/tests/schism')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('rompy/tests/schism/test_data/hgrid.gr3')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(HERE / \"test_data\" / \"hgrid.gr3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "\u001b[0;34m\u001b[0m        \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescription\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Whether to create a symbolic link instead of copying the file\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0m_copied\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrivateAttr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestdir\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Copy or link the data source to a new directory.\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Parameters\u001b[0m\n",
       "\u001b[0;34m        ----------\u001b[0m\n",
       "\u001b[0;34m        destdir : str | Path\u001b[0m\n",
       "\u001b[0;34m            The destination directory to copy or link the data source to.\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Returns\u001b[0m\n",
       "\u001b[0;34m        -------\u001b[0m\n",
       "\u001b[0;34m        Path\u001b[0m\n",
       "\u001b[0;34m            The path to the copied file or created symlink.\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mdestdir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlink\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m# Create a symbolic link\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0msymlink_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdestdir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0msymlink_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdestdir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m# Ensure the destination directory exists\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mdestdir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m# Remove existing symlink/file if it exists\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0msymlink_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0msymlink_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m# Compute the relative path from destdir to self.source\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mrelative_source_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m# Create symlink\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrelative_source_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymlink_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_copied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msymlink_path\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0msymlink_path\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;31m# Copy the data source\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;31m# Copy directory\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0moutfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopytree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0moutfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdestdir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0moutfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdestdir\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mif\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0moutfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_copied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m           ~/rompy/rompy/core/data.py\n",
       "\u001b[0;31mType:\u001b[0m           ModelMetaclass\n",
       "\u001b[0;31mSubclasses:\u001b[0m     DataGrid"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DataBlob??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rompy.schism.grid:drag is being set to a constant value, this is not recommended. For best results, please supply friction gr3 files with spatially varying values. Further options are under development.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grid object\n",
    "\n",
    "from rompy.schism import Inputs, SCHISMGrid\n",
    "\n",
    "#SCHISMGrid?\n",
    "\n",
    "# Medium sized grid, will run one day in about 3 minutes on 48 cores\n",
    "hgrid = HERE / \"test_data\" / \"hgrid.gr3\"\n",
    "\n",
    "# Fast running grid, will run in about 1 minute on 4 cores\n",
    "#hgrid = HERE / \"test_data\" / \"hgrid_20kmto60km_rompyschism_testing.gr3\"\n",
    "\n",
    "grid=SCHISMGrid(\n",
    "    hgrid=DataBlob(id=\"hgrid\", source=hgrid),\n",
    "    drag=1,\n",
    ")\n",
    "\n",
    "grid.plot_hgrid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('schism_procedural')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.schism.grid:Generated albedo with constant value of 0.15\n",
      "INFO:rompy.schism.grid:Generated diffmin with constant value of 1e-06\n",
      "INFO:rompy.schism.grid:Generated diffmax with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated watertype with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated windrot_geo2proj with constant value of 0.0\n",
      "INFO:rompy.schism.grid:Generated drag with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/hgrid.ll\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/hgrid_WWM.gr3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('schism_procedural/hgrid.gr3'),\n",
       " PosixPath('schism_procedural/albedo.gr3'),\n",
       " PosixPath('schism_procedural/diffmin.gr3'),\n",
       " PosixPath('schism_procedural/diffmax.gr3'),\n",
       " PosixPath('schism_procedural/watertype.gr3'),\n",
       " PosixPath('schism_procedural/windrot_geo2proj.gr3'),\n",
       " PosixPath('schism_procedural/drag.gr3'),\n",
       " PosixPath('schism_procedural/hgrid.ll'),\n",
       " PosixPath('schism_procedural/hgrid_WWM.gr3'),\n",
       " PosixPath('schism_procedural/vgrid.in'),\n",
       " PosixPath('schism_procedural/wwmbnd.gr3'),\n",
       " PosixPath('schism_procedural/tvd.prop')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.get(workdir)\n",
    "list(workdir.glob('*'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Forcing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First lists import the main data classes\n",
    "from rompy.schism.data import SCHISMDataSflux, SCHISMDataOcean, SCHISMDataWave, SCHISMDataTides\n",
    "\n",
    "# Sets also import a few of the minor classes that are used in the construction of these main classes for use in this demo\n",
    "from rompy.schism.data import SfluxSource, TidalDataset, SfluxAir, SCHISMDataBoundary\n",
    "\n",
    "# And also lets import some of the core data source objects. These are data input abstractions that work in exactly the same way as \n",
    "# with the swan classes, and can be used interchangeably in each of the data classes depending on the data source. We will use a \n",
    "# bit of a mix here for illustration purposes.\n",
    "from rompy.core.data import DataBlob, SourceFile, SourceDataset, SourceIntake, SourceDatamesh\n",
    "from rompy.core.boundary import SourceWavespectra"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sflux Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rompy.schism.namelists import Sflux_Inputs\n",
    "# SCHISMDataSflux??\n",
    "# SfluxSource??\n",
    "# Sflux_Inputs??\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geocollection.GeoQuadMesh at 0x14a780d6a920>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import intake\n",
    "cat = intake.open_catalog(HERE / \"..\" / \"data\" / \"catalog.yaml\")\n",
    "ds = cat.era5.to_dask()\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds.u10[0].plot(ax=ax, transform=ccrs.PlateCarree(), cmap=\"RdBu_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.schism.data:Fetching air_1\n"
     ]
    }
   ],
   "source": [
    "# Lets have a look at an flux object. Here we will use a ERA5 dataset exposed through the intake catalog in the tests/data folder.\n",
    "from rompy.core.time import TimeRange\n",
    "\n",
    "atmos_forcing = SCHISMDataSflux(\n",
    "    air_1=SfluxAir(\n",
    "        id=\"air_1\",\n",
    "        source=SourceIntake(\n",
    "            dataset_id=\"era5\",\n",
    "            catalog_uri=HERE / \"..\" / \"data\" / \"catalog.yaml\",\n",
    "        ),\n",
    "        filter={\n",
    "            \"sort\": {\"coords\": [\"latitude\"]},\n",
    "        },\n",
    "    buffer=2\n",
    "    )\n",
    ")\n",
    "atmos_forcing.get(destdir=workdir, grid=grid, time=TimeRange(start=\"2023-01-01\", end=\"2023-01-02\", dt=3600))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('schism_procedural/hgrid.gr3'),\n",
       " PosixPath('schism_procedural/albedo.gr3'),\n",
       " PosixPath('schism_procedural/diffmin.gr3'),\n",
       " PosixPath('schism_procedural/diffmax.gr3'),\n",
       " PosixPath('schism_procedural/watertype.gr3'),\n",
       " PosixPath('schism_procedural/windrot_geo2proj.gr3'),\n",
       " PosixPath('schism_procedural/drag.gr3'),\n",
       " PosixPath('schism_procedural/hgrid.ll'),\n",
       " PosixPath('schism_procedural/hgrid_WWM.gr3'),\n",
       " PosixPath('schism_procedural/vgrid.in'),\n",
       " PosixPath('schism_procedural/wwmbnd.gr3'),\n",
       " PosixPath('schism_procedural/tvd.prop'),\n",
       " PosixPath('schism_procedural/sflux'),\n",
       " PosixPath('schism_procedural/sflux/air_1.0001.nc'),\n",
       " PosixPath('schism_procedural/sflux/sflux_inputs.txt')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(workdir.glob('**/*'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geocollection.GeoQuadMesh at 0x14a779a5afe0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a map\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "# load the data\n",
    "ds = xr.open_dataset(\"schism_procedural/sflux/air_1.0001.nc\")\n",
    "wind_speed = np.sqrt(ds.u10**2 + ds.v10**2)\n",
    "# plot the data\n",
    "wind_speed.isel(time=2).plot(ax=ax, transform=ccrs.PlateCarree())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ocean Boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SCHISMDataOcean??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x14a779ac7910>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VIYSZVMZh3LpG4Hn7/IAhhFgnqwv+Fy5cgIeHBwRBKHKlP0IqG/+abKvsEUKKxzpin0b7W4nQ0FCcOnUKK1eutHRVCDGLhNs50Oax7zxGCCmMN8Nhj6wu+G/evBlRUVHYvn27patCiFlcPpoGWrOKEGJNrK7bn+d5aLVa5OXlWboqhJhFp7ergO5gEVI+DIyj/VnSVmZWFfx5nseAAQPw+++/Y8eOHZauDiFmIZFQ5CekvBgEMO7qZ766VCZWFfx37NiBLVu2YOvWrXj11VdNnpMyzvfN1ImfY52mZxuwpRHYJvprBNZ57ozbqjLOF07nLfvzSzeILz+XZ9vZjLXurGQStrEGjCtcMHOUsc0Xz5Iw3G9hDAo6xh0dM/PZ1gnQObP93apl4rfSrsh5/kQcqwr+33zzDVq3bo0+ffpYuiqEEEIqAdZBe/Y6HMeqgn9+fj78/PwsXQ1CCCGVBA8OBoi/tcYzpK3MrGq0P83tJ4QQQsqf1QR/QRAQGxsLFxcXS1eFEEJIJcEL7Ic9sprgf+HCBdy6dQs9e/a0dFUIIYRUEoZ/uv1ZDntkNcG/SpUqUCgUmDx5Mlq3bo2//vrL0lWyW0cO5uHU77SlMiHE+lHwF8dqgr+Pjw++//57+Pv74/bt2xg6dKilq2S3goJlyM62074wQgixA1Y12r93797o3bs3vvvuOwwcOBAJCQkICAgwS955evHzrR/r2Oaps+4Jr5aw7estKePGFVVqy1ClNqD5J1mi3pWp/FyebR/7HMb0Wbz4+dKse6Kztip4wbKtEpY93QFAJ0iZ0ns4sL33s7Ti3zsZErZ59rye7XeXk8+2xkSWnq3+nvIc0WmlFTiBjhc4pr8TS/+NWYrVtPyfFRUVBU9PT3Tv3h2PHz+2dHUIIYRYKer2F8cqg7+XlxcOHz6M+Ph4vPXWW5auDiGEEGJi+fLlqFatGpRKJZo3b44zZ8489/olS5agTp06UKlUCAoKwvjx46HRWG5slVUGfwBo3Lgx/u///g+//PILLl++bOnqEEIIsUIGSJiPstqyZQsmTJiAmTNn4vz58wgNDUVUVBSSk5OLvP7777/HlClTMHPmTFy7dg1r167Fli1b8NFHH7G+fNGsNvgDQJ8+fdCgQQN0794dfD6tFU0IIcSU8M89f7GHIOKe/6JFizB8+HBER0ejfv36WLlyJRwdHbFu3boirz958iRatWqFN998E9WqVUPnzp3xxhtvlNhbUJ6sOvirVCps3boVcXFxeHzkqqWrQwghxEZlZmaaHPn5RW9spNVqce7cOXTs2NF4TiKRoGPHjjh16lSRaVq2bIlz584Zg/3du3exb98+dO3a1fwvpJSsOvgDQP369TF48GDErTyEjHN3LV0dQgghVsRcA/6CgoLg5uZmPObNm1dkeampqTAYDPD19TU57+vri8TExCLTvPnmm/j444/x0ksvQS6XIyQkBO3ataNu/5KsWLECLo2DcXPGViT/fA5CGaeuEUIIsU0GQcJ8AEB8fDwyMjKMx4cffmi2Oh49ehRz587FV199hfPnz2Pnzp3Yu3cvPvnkE7OVUVZWNc+/OCqVCrVmvo641b8idvlBJO+9gCrR7aBuVrPUeWh04uf5p2jY9htIcWRL7y3NZErPOuWWdZ4+yzx7AEg3ODKlzzWw1Z+FXKK3WNkAIGXclN6Bsf4qjm2sjiPjvvDpWvFrbKTJ2d53Ognbx6tGK/4zCwDS89nWF9Epxa/RoBMq30a5rq6ucHUteU0TLy8vSKVSJCUlmZxPSkoqdlfa6dOn46233sKwYcMAAI0aNUJOTg7eeecdTJ06FRJJxbfDK0XLHwA4qQTBIzujzoI3IXNR4c7c3TBoaBAgIYTYMx4ceEgYjrIN+FMoFAgPD0dMTMy/deB5xMTEoEWLFkWmyc3NLRTgpdKCL1eW6smuNMH/KdfGwQh6pwP4fB0e/3qZbgEQQogds8QiPxMmTMCaNWuwceNGXLt2DSNHjkROTg6io6MBAIMGDTK5bdCjRw+sWLECmzdvxr1793Do0CFMnz4dPXr0MH4JqGiVotv/vxxDfOER2QCxyw8g8+J91Jz2mqWrRAghxAKevW8vLn3ZG5D9+vVDSkoKZsyYgcTERISFhWH//v3GQYBxcXEmLf1p06aB4zhMmzYNDx8+hLe3N3r06IE5c+aIrjerShn8OY5DyORX4BoajPtL9iH7+kM41w20dLUIIYTYidGjR2P06NFFPnf06FGTxzKZDDNnzsTMmTMroGalU+m6/Z/l1bERHPzUiF9zGPqsvELPC4IAXVo2BJ5uDRBCiC0quOfPdtijSh38OakE1cZ3Q158Ku7M3Q1BEJBy8BIyL9yHIScf1yZuwsUBy3D5nVUw5BS9YAMhhJDKi2dc2pev3GFQtErZ7f8siYMMnEyKrMtxSPj2GBK+PwEAkDoVTO9yCHRH/sMn0GdrjOcIIYQQe1Zpgv+ZLnOLPN/1i6649iQHarUaCd+fwOzZsyGVSnHixAlMnz4dLVu2REREBGT+LhA74f1RLts8/VQXtvS5CrYvLTqwjSYVs/GFOfEMg3kAMHXraXi2udZSXobkWxnwqu4CiazytTBY5/k7Stl63Fh/965y8X97Sjnba8+Ts811F3i27ugsHdvnRj4vPjzkV+CtVksM+LMFle/T6D/69OkDAEhPT8e4ceMwY8YMTJ06Ffv27YNCoQAADB482JJVJHbo3skk5Dwu2K7z6NKriD2XauEaEWKb2Ob422+3f6V/1dHR0XjrrbcAAA8fPjSef/ToETp06ICwsDAMHTrUUtUjdkgQBHAS4M7vBet89/miBYIjvC1cK0II+VelD/5AwY5JAJCRkWE8N3/+fHAch5iYGKhUbMtcElIWHMfBr747Gr4SXPBYwkEitc8RxYSUN4PAMR/2qNLc83+eESNGoEuXLggICAAA6HQ6rF+/HmPHjoWHh4eFa0fskdJV8c//7PN+IiEV5emoffHp7fNv1CaCP8dxqFatmvHx3bt3kZWVhfbt21uuUoQQQoiVsolu//9yc3MDx3GYM2cOMjMZd8QjhBBitXhBwnzYI5t81X5+fvjmm29w+vRps+7JTAghxLqwLPDDesugMrOJbv+iDBw4EI8ePcKHH36IMWPGwEEqfs6uRs+4r7aObV/wTIOSKb2rVMOU3kli2dUR0zm2+dIsc/V51sFAHNsHi5wzMKXXSdjSs5av5Njmyhs4tp+/i1z8e99Jwfa+z5YrSr7oOQw823tHq2f7eGeZ56/l2f5my4IHmAbtVVxNrYtNf+V57733UKVKFUyePNnSVSGEEEKshk0Hf6VSidmzZ2PPnj3Ie5RRcgJCCCGVCi3yI47Ndvs/9eqrrwIAMv9OgMrfzcK1IYQQYk7sy/vaZ/C3+VetVqvh6OgI7ZMcS1eFEEIIsQo2H/wBIDw8HEmH/gavYxu8RAghxLrw4JgPe2QXwX/p0qXIvf8YsZtOWboqhBBCzOhptz/LYY/s4lU3adIEwW+1QNzmM9Ak0sA/Qggh9s3mB/w9VaNfKB5s+xNJey+g9oiXypQ2V8c2XzfbwJY+l2fbl5t1nr5amsuU3pFjKz+XZ/v5perE7+nOMtcZAOQStlnEcsZ5+o4SLVN6pUTHlF7OOM/fgWNbd91VJn6ev7Oc7WeX6cD2s9Pq2N57Mql93OZkX9vfLtrAhdjNq5ap5Ah4uT4e7r0KfS7bHzUhhBDrwAsc82GP7Cb4A0Dw603A6ww4P3k3ch+kW7o6hBBCiEXYVfBX+bki/PNeyH2YgUuf7rd0dQghhDDiGdf1t9dFfuzuVasb+MOvQ20YqOufEEIqPdrVTxy7GfD3LEOeDlJHtkFkhBBCLM8ADgaGufosaSszu/zKI3WQgc9nG4VMCCGEVFZ22fIHAH0e2zQcQgghlsfadU/d/jZO9sye8Lr0XPB5WkgFAzhJyV0+Usa52rl6tnn6rPP8WeexujCuE8C6p7sOUqb0STrxGzrlGeRMZbPsMw6wrzMgYZwn78A4z99FKn6evTmwrHPgKGN77U4KtnFFUtbfndQ+ejcNYOu6t4/VEAqzy688wT0bQZuhwd3N5y1dFUIIIaTC2U3L/1keoYEIGRCOm1+fgtLLCYGd61q6SoQQQkSgbn9x7DL4A0DtoS8i/0ke/ppzCDJnB/i2rG7pKhFCCCkj1s15aGMfO8NJJWg0uT3c6vog/uerlq4OIYQQUmHsNvgDQOqZOGTfT4PSy9nSVbELZ//Ix+WLtLgSIcR8BHDgGQ7BTuf52223f15yNs5N3wfPJoGoM6KlpatjF+o1lCMvl20EMyGEPIu6/cWx2+CfduEB+Hw9wqZ1htyJVvurCE7OEjhRJwshhFic3QT//+7NrUDBYzdXARLZ87ui0zSOTGVn69i+XKTpnZjSe8iymdJLwbbOgQtXMJN29coc5OcLeG9s2b4BVJM9YSo/Ua4WnfYJ48+edZ6/TmBb48BdmsuU3kXCNk9fzVh+pkHJlF7JsE6BuyKHqexcPdsaEazz9JWM6VnWiCjF8ilmw7otr71u6Ws3wf+/ZKqCP0xdthYOapWFa2MfOnRygEpln39ohJDy8XR3Ppb09sgug78gCLi9629IHWTgZPb5i7eEkBC7fLsRQsoRtfzFscvIp83MR8r5BHjU94bCmW3pXEIIIaSyscummIObEuraXpAq7fLlE0KIzeAhAc/QjmVJW5nZ56sG4PtCIBJPxSPzPttgMiLOb0fzsWYV24AqQggxCBzzYY/sNvh7NvQFAOQ/ybNwTeyTTAa4udnnHx0hhFia3fZ7p996DAe1Et5NAixdFbvU6iUaa0EIYUcD/sSxm+Cv5U3nS6ffS4djoFuh80VhnW+bwzjP/1G++P3oAcBDxta9ni5nmwrpLWXbF92bsX8qRJEsOm2S3pWp7Mc6F6b0Kob96AHAV57BlN5Pns6UXsmx/e5Zu2TdGNYZ8FawrY+hZ1yj4YmWbX0RCdhW09SX4rOx+LQGprLLQmDc1U+w0xX+7PJV6zU6pPz5AJ6N/S1dFUIIIaTC2U3L/1kPfr0FXU4+qvdsYOmqEEIIYWAABwPD5jwsaSszuwv+vJ7Hnc1/wbdFMJwC2brTCSGEWBYvsN235+10rzG76/aP/fkasu4/Qb2hzSxdFUIIIcQi7Krlr8vV4vraMwiKqg11HW9LV4cQQggjnnHAH0vaysyugv+dLX9Bl6NFveHNLV0VQgghZsCDA89w354lbXnZs2dPmdN06tQJKlXpZ2bZTfDf1Wo5WrzfAv1698X3vb8pU9pOR8czlZ3HuLXno1y26WYqhm1NAcBFyrYQkqeEbVtXbynbtKEAhi2Nq8jTmMpm5SFlm6ZZg2GaIwD4SNmmu2kEto8YnYRtupyB4Xefy7OtRcG6HTPrVL08A9vnTmXBukqfNa7w17NnzzJdz3Ecbt26hRo1apQ6jd30dyQlJeHPP/9E27ZtLV0VQggh5LkSExPB83ypDkfHsq8JYTct/1OnTsFgMKBHjx6WrgohhBAzscV7/oMHDy5TF/7AgQPh6lq2HmK7Cf5SaUEXHM/zxV6Tn5+Pq1evomnTphVVLUIIIQx4MC7va4X3/NevX1+m61esWFHmMqzvK085adasYGrfrl27ir3mf//7H8LDw5GSklJR1SKEEEIqnN0Ef3d3d8hkMmg0GgDA1atXsXDhQuzevRtabcH66UeOHAEA+Pj4gOM4vPfeexarLyGEkJIJ/4z2F3sIIlv+y5cvR7Vq1aBUKtG8eXOcOXOm2GvbtWsHjuMKHd26dSuxHI1Gg4ULF6Jr166IiIhA06ZNTQ6x7KbbXyaToUaNGjhw4AAmTJiADz/8ED/99BOAgmDfv39/JCYmwt/fH++//z4mTpyI27dvW7jWhBBCnscSu/pt2bIFEyZMwMqVK9G8eXMsWbIEUVFRuHHjBnx8fApdv3PnTmMjEwAeP36M0NBQ9OnTp8Syhg4dioMHD+L1119Hs2bNwHHmuU1hN8FfIpFg6dKl6NatG8aOHQtXV1f4+Pjgiy++wKlTp7Bjxw7k5eVh+PDhCAoKAgDMmzfPwrUmhBBibRYtWoThw4cjOjoaALBy5Urs3bsX69atw5QpUwpd7+HhYfJ48+bNcHR0LFXw//nnn7Fv3z60atXKPJX/h90EfwDo0qULVqxYgREjRuCjjz6Ct7c3Bg0ahClTpuD27duIj49HYGAgVqxYAaVSiQYNCjb+cZSxzZPXGdjm+6bkOjOl1xjYfs0yCds8ewfGbV3rOiQypXdiKL8q4zx/HeM8d0+GeeoAUFXGtqWvh5StlZFoYPvdawS2ueoGSb7otKzrW7BuGMO6z7xMwrZOAQteUvzAarOXVcGj/bVaLc6dO4cPP/zQeE4ikaBjx444depUqfJYu3Yt+vfvDycnpxKvDQwMhIsL29bgRbGbe/5PvfPOO5gxYwbmzp2LMWPGYNKkSZg3bx4GDhyIkJAQqFQqdOzYEVqtFh988AH0er2lq0wIIaQYT7v9WQ4AyMzMNDny84v+4piamgqDwQBfX1+T876+vkhMLLmhcubMGVy5cgXDhg0r1ev7v//7P3zwwQeIjY0t1fWlZXfBHwBmzZqFYcOGYcSIEThy5AiaNGmC7du344cffgAANG7cGIsXL8bixYuhVquRfoNtlTRCCCHWLSgoCG5ubsajvG77rl27Fo0aNTLOQCtJREQENBoNatSoARcXF3h4eJgcYtlVt/9THMdh9erVaNWqFQ4cOIC7d+8CACZOnIj+/ftDIpFgzJgxyM/Px+TJk/Hk7ySo6xQexEEIIcSyzLW2f3x8vMlCOQ4ORd828fLyglQqRVJSksn5pKQk+Pn5PbesnJwcbN68GR9//HGp6/fGG2/g4cOHmDt3Lnx9fWnAHyuO4zBkyBAMGTIEGRkZUKvVSExMxN27d1GzZk0ABdMDAeDBwZuo3quRJatLCCGkCOYa7e/q6lqqVfIUCgXCw8MRExNjXIOf53nExMRg9OjRz027bds25OfnY+DAgaWu38mTJ3Hq1CmEhoaWOk1p2GW3/389+0u/ePGi8XytWrUAABIp/ZgIIcQameuef1lMmDABa9aswcaNG3Ht2jWMHDkSOTk5xtH/gwYNMhkQ+NTatWvRs2dPeHp6lrqsunXrIi+PbfBpUey25f8sjuPQt29fbNiwAatWrUKtWrXQsGFDLF26FLVr10btxe0tXUVCCCFWol+/fkhJScGMGTOQmJiIsLAw7N+/3zgIMC4uDhKJaaPxxo0bOH78OA4ePFimsubPn4+JEydizpw5aNSoEeRy0xkwZV3T/ykK/v8YOHAgvv76a5w6dQphYWHG8xs3bsQOefErNxFCCLEcSyzyAwCjR48utpv/6NGjhc7VqVMHglD2bZq7dOkCAOjQoYPJeUEQwHEcDAZxU7Ep+P8jJCQEQMGAjOrVqyMvLw8hISGQyWRw5PIgkYnv+medZ5+rUzClf5Qh7pvhU6zzjVll8aXf3aoo1RTi92pw4TRMZXvLMpnSu0rYyvdivGXlzLG993I4tu7KdLDNF1cyrPHAuj6FFGX/oH+Wl5xtjQcHCds0ZZa/+3zGtVHKwlLBv6I8XXbe3Cj4/8PPzw/z5s2Dh4cHdu3ahbt37yIrKwsDBgzAS3M7oGr76pauIiGEEDvTtm3bcsmXgv8/ZDKZcVnGd955B0DB+steXl7gJNb9zZAQQuyVALZtedn6Z8rHpUuX0LBhw0LjBopz9epV1KlTBzJZ6UM6DWN/jqdb+ypc2Lo+CSGElA9LjPYvb02aNMHjx49LfX2LFi0QFxdXpjKo5f8cV69eBQC4VlNbtiKEEELshiAImD59OhwdHUt1/bM7BpYWBf/nOHDgAGrWrAmlB9uAM0IIIeXDFgf8tWnTBjdu3Cj19S1atIBKVbY4RcG/GFlZWdi6dStGjhyJOC71udcKggBDvgEyJf04CSGkItli8C9qqqC50T3/Yqxbtw45OTkYNWpUidfe3HoVW9ttwJ09N0TN4ySEEEIqEjVVi7Fy5Ur07dsXVapUgU9q1nOvjRMK5jKfnnsMKn02wt6obfJ8en7lvm2QyLhOwDmGvbYBINmZbS/rZCfx9a+nTGAqm3Wev4RxnjvPOJaZNb2UsVEl5RhfP8N7T8qxvXaNwPbxquTY5um7yXKZ0rOsU6CRV9xW6LbY8q8I1PIvgk6nw82bN9GuXbtSXZ/0dxrUVZ3RuG9NHP/iEn4YeAi/fX6BegEIIaScCQLHfNgjavkX4eHDh+B5HlWrVi3V9ak30pEel430+NuAACRdTUPS1TS0HhcKTmafbyxCCKkI5trS195Q8C/C0/mSpQ3+HaZHIP5MMuSOMiRefgzeIKDpwNpMSwITQggh5YWCfxGeBv+goKBSXV8l3AdVwn3Ks0qEEEKKYA/3/G/duoUjR44gOTkZPG86DmbGjBmi8qTgX4RLly7B19cXzs7Olq4KIYSQ52C9b2/t9/zXrFmDkSNHwsvLC35+fuC4f+vLcRwFf3O6ePEiGjdubOlqEEIIsXOffvop5syZgw8++MCs+dJN6SL07NkThw4dwk8//WTpqhBCCHkOW1zb/1lPnjxBnz59zJ4vtfyLMHLkSOzatQsjR45E7dq14Sgp+7rJz3JVsO3JnqFVMqWXSuRM6bV6tu+ICY/dmNInZ7DN809Qi5/n/8S9dGtrF6epcyxT+mry568uWZIsnm2evIHLZ0qvY5ztKmec589Svk6QMpUdr/FgSu8pz2FK769IZ0rvJBH/u8+TVdw8f1vv9u/Tpw8OHjyId99916z5UvAvAsdxWLt2Ldq3b49x48ah6YLKvUgPIYSQyqlmzZqYPn06/vjjDzRq1AhyuWljbsyYMaLypeBfjKpVq2LWrFl46623kOpeA+3GNIDCkX5chBBiTQTGrntrb/mvXr0azs7O+O233/Dbb7+ZPMdxHAX/8jBgwACkpKRg8keTcPv3RHSd1RTVmnlbulqEEEL+IQBgWUzV2tdhvXfvXrnkSwP+noPjOIwfPx7DtrWHi58K3w8/jit74y1dLUIIIXZIEASzLRtPwb8UPKo6Y+DXrdGoRxB++eQCMhLYNswghBBiHk+X92U5rN0333yDRo0aQaVSQaVSoXHjxti0aRNTnhT8S4mTcOg8JRRKVwV2TDiNlNtsu7URQghhZ+sb+yxatAgjR45E165dsXXrVmzduhVdunTBu+++i8WLF4vOl+75l4GDsxx9l7XAtjGnsKZ3DDyrO6P5oFoI7RVssupScTRpuUi9nAy9Rg//F6vAwY1tCh8hhNg7XuDA2fDyvsuWLcOKFSswaNAg47lXXnkFDRo0wKxZszB+/HhR+VLwL4U5jXf++6Ax8GUPDfbv348ffvgBW2dvRQvZa5g2bVqx6Xv/2A+n5h5H0oVE8LqCecv132yIiLHNSlW+u0MeU/2zNA5M6XkD2zoBeg3b24x1xvD9PC/RaZ/ksc3zz/NVMKVXqnVM6dUStveOQWJgSs/6waoAW/ksN+hyeLa/m9hstnn+WQ5sjYNgB7Y1IlwY3jtSju33Rv716NEjtGzZstD5li1b4tGjR6LzpW5/EZRKJXr27IktW7ZgxowZmD59OurWrYtNmzYV2nQBAOKPxeHRmQQEta6Kev3qAwAMWvrjIIQQVoLAflizmjVrYuvWrYXOb9myBbVq1RKdL7X8Gc2YMQPVqlXDTz/9hEGDBuGrr77CggUL0KpVK5w/fx7btm2D3EkOpbsSKVdSkJtcsGqXa9XCq87d2nMTfk394FJF/Ip0hBBiT2x9hb/Zs2ejX79++P3339GqVSsAwIkTJxATE1Pkl4LSouDPSCqVIjo6GtHR0Thy5AjGjh2Ltm3bQqlUQqPRgOM4CIKAZpNeRMIfCajbtx4863nBN8zPJB+9Ro9Tc44DAAYcHwypnG1pUUIIIZVf7969cfr0aSxevBi7d+8GANSrVw9nzpxBkyZNROdLwd+MIiMjceHCBZw6dQrHjx9HWFgYOnTogICmgfj72yvovKIrnP2L3iZYpvz3V3Ft899o+Fajiqo2IYRUWrbe8geA8PBwfPvtt2bNk4K/mUmlUrz00kt46aWXjOfafBqJg6N+wS9Df0LT0S+gRpcQcBLTN1xmXAbAARCAv7+7jDq960LuyDbQjhBCbJ0tjvbPzMyEq6ur8f/P8/S6sqIBfxXA2d8ZL6/pBu9GPjgx+3ccnnAIes2/Y9izHmTi0JgDcAl0Qf03G0Kv0UPgrXwUCiGEkHLh7u6O5ORkAIBarYa7u3uh4+l5sajlX0FUno5ot6ADHv7xAL9NOYyfB/0I/xcCkHolGen30uHo44TOy7vgjwWn4BPmC4Vz2aaIPb74EFKVHOo6PuX0CgghxPqwjti3xtH+hw8fhodHwVTRI0eOlEsZFPwrwMZma/990Ay4HHkZkyZNws1zN9GlZRcEvxaMSZMmQa1WY/D2wTh8+DAWhXwGT09PAEDUb+NKLOPUmF0AgHrjIxH0amOT51RytpnyuXls853B2q3G+Mcp8OLLT08teoxGaZ3lgpjSq6RapvQuarZ5/oHIYEpvYFw6VSewDXxlKT/bwDbP/mGWG1P6XD3jbT/xjUIAgFIifo0JnnF9iLIoCP4s9/zNWBkzadu2rfH/1atXR1BQUKGF5ARBQHy8+L1mKPhbQKNGjbB///4in5s1axb27duHN998E/v37y/VyoEAULV3GOJ2XMS1xUegbugPlxDafZAQQiq76tWr49GjR/DxMe3VTUtLQ/Xq1WEwiPuiRff8rUz16tWxceNGHDx4EPPnzy91ujqj26DWOwVzQC/PPVhe1SOEEKti62v7C4JQZCMwOzsbSqX43ilq+Vuhrl27Yvr06fjoo4+watUqaOuo4FrHF+6NAuBS0xu8zoDse4/BSThwUg6cRAJOKoFb/YK1A7LvpEKbkQeFm8rCr4QQQsqXALY7g1bY6w8AmDBhAoCCreWnT58OR8d/lxo3GAw4ffo0wsLCROdPwd9KzZo1C1euXMGPP/4IZ6UXEo/cgqAzwLGKGgaNDvmpOUWmk7upoMvIw8Nf/kb1/uEVXGtCCKlYtjrP/8KFCwAKWv6XL1+GQvHvIHCFQoHQ0FC8//77ovOn4G+lJBIJtm3bhszMTPS/NBuGfD3SryTg0aHrkMilCIiqB4lcCoEXIBh48AYBHAe41ffD1YUxiN99CcGvhUKioF8xIYRUNk9H+UdHR2Pp0qWi5/MXhyKDFZNKpcZ5nFIHGTzDq8IzvGqJ6ar1bYLEwzdx9f8Oo+GUTuVdTUIIsRxb7ff/x/r168slXwr+NsglxBsNP+iIy58egFOgGp5925aciBBCKiPWQXtW2u3/rLNnz2Lr1q2Ii4uDVms6/Xfnzp3FpHo+Cv6VwIG2S8qeqC3wsfxjzJw5E/Lq/vB4MUR0+RzH+NWYNb2Wba430982Y93TUl2Y0p+Vl9zT8zye8qLHhpSW1Ok2U3olx7bGhASFt8guC5Z1ArIY5/ln57Ctj8EzrE8BABqebZ0AOcTP1ZcxpCWmNm/ejEGDBiEqKgoHDx5E586dcfPmTSQlJaFXr16i86WpfjZs+vTpaNeuHRJ2nLV0VQghpFw8XeGP5bBmc+fOxeLFi/HTTz9BoVBg6dKluH79Ovr27YuqVcU3Dij42zCO49CyZUvkxj2GYO3vcEIIEcHW5/nfuXMH3bp1A1Awyj8nJwccx2H8+PFYvXq16Hwp+Nu41q1bQ5eWg5w7yZauCiGEkDJyd3dHVlYWACAwMBBXrlwBAKSnpyM3N1d0vhT8bVzHjh2h8HbBg+//sHRVCCHE/ASO/bBibdq0waFDhwAAffr0wdixYzF8+HC88cYb6NChg+h8acCfjZPJZKjStxnuLo+BPksDmQvbICZCCLEmtrir37O+/PJLaDQaAMDUqVMhl8tx8uRJ9O7dG9OmTROdLwV/OyBRFoz6Leq+v8ALAIdSbyBECCFWxcbn+T/d2hcoWPxtypQpZsmXuv1tXHJyMh79eB4AkHEhzuQ5QRDw5xsrcP7ttbi/5igy/35IAwMJIcSKdOzYERs2bEBmZqZZ86WWv41LSEhAzu2CwX5rOr+P1q1bmzyvyv0SNQMCkXw0Fpe3/omxY8di8eLFJj0BdXZ+zFYJxk4F1ltynJYhAylj4flsyR8+YtuU/TdZTab0LlINU/pgRQpTek8p2zoFWkH8R1weryj5oufQa9g+XsUP5SqgEdjm+Ss48XP1dQxpy8pW1/Z/qkGDBvjwww/xv//9D926dcPAgQPRtWtXyOVsv19q+du4sLAwxMbGwtPTE59//jl43nTRFHd3d/Tu3RsJCQn47LPPsHTpUly7ds1CtSWEEBEEhsPKLV26FA8fPsTu3bvh5OSEQYMGwdfXF++88w5+++030flS8LcDVatWxfr16/Hzzz9j1KhRJl37Li4uuHfvHqRSKYYMGQIAuH79uoVqSggh5L8kEgk6d+6MDRs2ICkpCatWrcKZM2fQvn178XmasX7EivXo0QNr1qzBypUr8cEHHxi/APTt2xc7duxARkYGvLy84Obmhps3b1q4toQQUjq2vsjPsxITE7Fy5UosWLAAly5dwgsvvCA6L7rnb0fefvttZGZmYvz48bh//z6aNm0KQRCQk5ODDRs2YOzYsahduzauXr1q6aoSQkjp2Pho/8zMTOzYsQPff/89jh49iho1amDAgAHYsmULQkLE79lCwd/OjBs3DhKJBOvXr8evv/6KnJyCAVWxsbEAgK5du2LBggX4+OOPUb16dUtWlRBC7J6vry/c3d3Rr18/zJs3DxEREWbJl4K/HRozZgzGjBljfMzzvHF0/7hx47Bp0ya0bt0aW7ZsQatWrQAUTAvMPnkVvFaPvCt34dGrNRRVvC1Sf0II+RcHtilF1t3tv2fPHnTo0AESiXnv0lPwJyZvKrVajePHj6N3795o06YNoqOjken0BJo7CUjf++8SwTkXbiPok7ehCPSyRJUJIaSAjXf7d+rUCXq9HocPH8adO3fw5ptvwsXFBQkJCXB1dYWzs7OofCn4k0L8/f1x7NgxLFu2DIsWLUJifDyAgmmDW7duhVqtRmRkJDRL9+GL5ctRu3ZtBAUFQSYr+u1U/bt5TPURJIx/nRLx3+wl+WytAl7GWPcctj/Re4lsX87OqKoxpYcbW3K5A9t8cZ5hTHO+gfHjkXEgGW9ga+nxAlt6JacTndbA8SVfVMktX74cCxcuRGJiIkJDQ7Fs2TI0a9as2OvT09MxdepU7Ny5E2lpaQgODsaSJUvQtWvX55YTGxuLLl26IC4uDvn5+ejUqRNcXFywYMEC5OfnY+XKlaLqT6P9SZGkUinGjRuHuLg45OTkICcnB+fOnUOtWrXg7e2Nbdu2ITs7G126dEGNGjXg6uqK1157Dd988w0uXbpUbL60giAhxKxY5viL7DXYsmULJkyYgJkzZ+L8+fMIDQ1FVFQUkpOL3j1Vq9WiU6dOuH//PrZv344bN25gzZo1CAwMLLGssWPHIiIiAk+ePIFKpTKe79WrF2JiYspe+X9Qy5+UyNHRsdC5evXqIT4+HnFxcbhz5w7Onz+P3bt3Y/DgwQCAkydPokWLFiZpNDdjkfjxKrh0aAaPIa/SfgKEEHasO/OJSLto0SIMHz4c0dHRAICVK1di7969WLduXZFr769btw5paWk4efKkcWW+atWqlaqsY8eO4eTJk1AoTFecrFatGh4+fFjmuj9FLX8imlwuR0hICDp37owpU6bgjz/+wPbt2wEUdHH9lzb2EQAgK+YMtHfFv2kJIeSpp7v6sRxAwZS6Z4/8/KLX5tZqtTh37hw6duxoPCeRSNCxY0ecOnWqyDR79uxBixYtMGrUKPj6+qJhw4aYO3cuDIaSb2vxPF/kdQ8ePICLi0spfkJFo+BPzKpnz55wdnZG165dkZCQYPKcooqP8f98bp5ZyuNz8yDwtn9/kRBSvoKCguDm5mY85s0reqxSamoqDAYDfH19Tc77+voiMTGxyDR3797F9u3bYTAYsG/fPkyfPh3/93//h08//bTEenXu3BlLliwxPuY4DtnZ2Zg5c2aJ4wWeh7r9iVmtXLkS2dnZAID4+HgEBAQYn+N1euP/FdUDYcjKxZOtB+AYXh+OYXXKVI4uIQnaB4l4smkXpJ7u8Js2ClwxAw4JITbMTKP94+Pj4erqajzt4ODAVK1n8TwPHx8frF69GlKpFOHh4Xj48CEWLlyImTNnPjft//3f/yEqKgr169eHRqPBm2++iVu3bsHLyws//PCD6DrRpyUxq6f38bdu3YrmzZubPJd15M+C/0glSN/xK/Iu34Y+MRW6hJQyBX8+JxePZi0F/ukK43PzIGh1FPwJsUdmuufv6upqEvyL4+XlBalUiqSkJJPzSUlJ8PPzKzKNv78/5HI5pFKp8Vy9evWQmJgIrVZb6H7+s6pUqYK//voLmzdvxqVLl5CdnY2hQ4diwIABJgMAy4o+LYlZOTk5AQCGDBmCGjVqIDw83PickJMHmb8XZO6uyLt8C1JXZ0hdnYxBvLT0qU9M0rj17ASJo/g/AkIIKS2FQoHw8HDExMSgZ8+eAApa9jExMRg9enSRaVq1aoXvv/8ePM8b11W5efMm/P39nxv4n5LJZBg4cKDZXgNAwZ+Y2eDBg9GpUye8+uqreO2113DixAncG/AhACDGrxn69OkDISEN8fHxcHZ2xtixY3HgwAFc/+eakvA8jwYNGsCrYUM0atQI27ZtQ+IPe6BUKotNU23F56JfD6dnm5HAOqGBY5wZyWez7fl9I82n5Iuew03ONrZDzulLvug5vGVZotMaGFd+k8jZ1ihQOLC9djnHVj5LelkFzvPnBLa/EzFpJ0yYgMGDByMiIgLNmjXDkiVLkJOTYxz9P2jQIAQGBhrHDYwcORJffvklxo4di/feew+3bt3C3LlzTVZafdaePXtKXZdXXnml7C8AFPxJOQgICMDOnTsRHh6Oxo0bY+HChXj99dexePFiZGRkQKVSGef7N23aFF988QXi4uIQEBCA27dvo27dusXmnZaWZtxy+MqVKwAK3vxffPHFc9MRQmyUBVb469evH1JSUjBjxgwkJiYiLCwM+/fvNw4CjIuLM1k5NSgoCAcOHMD48ePRuHFjBAYGYuzYsfjggw+KzP9pj0JJOI4r1YyBotBof1IugoKCcPXqVXTr1g3Dhg2DWq3G3r17wfM8XnzxReMUlV69esHX1xc1a9bECy+8gHr16mHIkCHGQYP/5eXlhaFDhxof+/j44NChQ1iwYEGFvC5CCAGA0aNHIzY2Fvn5+Th9+rTJGKejR49iw4YNJte3aNECf/zxBzQaDe7cuYOPPvrIZAzAs3ieL9UhNvADFPxJOfL29samTZvw22+/mZx/dglMV1dXbNmyBTqdDhcvXgQAbNy4ET///HOx+X799dc4c+YMZs+ejaysLAQGBuKbb75BWlpaubwOQogVezrgj+WwQxT8Sblr1aoVli1bhkmTJmHXrl2F5ra++OKLqF+/PtRqtfFcSEgIDAYD/ve//2Ht2rWFvuG+8MILiIyMRF5eHlq0aAGe57Fz586KeDmEEGtigeV9K0LXrl2RkZFhfDx//nyTxdMeP36M+vXri86f7vmTcieVSosdBQsUzKd9ev/+hx9+wIABA/Daa6/B3d0dly9fBgD89ddf+OKLL0zSPf3DyMvLg0KhwPDhw9GkSROTGQaEEFIZHThwwGSVwblz56Jv377GRpJer8eNGzdE508tf2IVOI4Dx3F49dVXARQsXRkQEIAJEybgjTfewLJly5Cammq8/vHjx8Z7/6GhocYxAl26dMG9e/cq/gUQQizDRlv+/90EzdybolHLn1gVJycn7Nu3DzVr1kStWrUAAJMmTQJQsAKXl1fBFrU3b9407qA1d+5c47fh1NRU9OnTB2fPnq34yhNCKp4FRvvbAgr+xOq8/PLLJo/3vwA4BHmhadOmCBrXAx4dQwEAddf8Dw9XH0TWn7cxefJkAIBEpUBSKz+E7Z3+bwa8WnRdWMcCSXSMGTB+MHEcW+deekbhHR3L4oaSbZ0AvVD0aOjSqu2YVPJFxWCdJ+/somFK76piS+8g0TGlNzB0DLP95MrIArv6VYSnvaH/PWcuFPyJ1ZM6KVFjVn/ELfkJid/9Zgz+Dv4eCBrbAzl/xwMGHpxCBrfmtS1cW0IIYScIAoYMGWLcY0Cj0eDdd981rqJa3K6DpUXBn1QKCl815J4uMGSZrhgnVztB3ZIW9yHEXllihb+KMHjwYJPHRS3vO2jQINH5U/AnlYY+LRtyT/H7VxNCbJCN3vNfv359ueZPo/2J1dFoNNi1axcePHgAAMg8exuxC3dB6qxE1sV7SD95/bnpr761BLELd1VEVQkhpFKi4E+szunTp/Haa68hKCgIu3fvxoPl+5D+21VknLwOZVVvxM7djtiFu6B7UngJYEFvgP5JNtJ/u2r2qTGEEGIrKPgTqxMWFmb8f69evaB7nIVqH70OAPDu2Qxuresj/beruPfxVpN0hjwtHh+4YHycefpmhdSXEGI5HP697y/qsPQLsBAK/sTquLm5YcKECXB2dsb+/ftR58t3IPdxAwDEf7EXqqreAABlsJdJurszvsfDFfsLnqvmA4dAz4qtOCGEVBI04I9YnaysLBw4cAARERGIioqCw8/HAQA1Fw/F7fFrkfhdwUZBT369BK9XmkNVvWAbTYm84O2srOGHWoveBieVQGAcCczaLOC0bBmwjkQW5GwZ8OkKpvQPFWqm9E/y2NYZyHBXiU7rrcxiKtvbueidKUvLwyGv5Iueg2ecv64TxLcN9RV5x81G5/mXN2r5E6uh0+nQvXt3BAUFIS4uDsuWLTN53rGmP0IWDoH/0E5wbloDDkFeEHT/LicSNOFVBLzbBTUXDAInpbc2IXbBRpf3fdamTZvQqlUrBAQEIDY2FgCwZMkS/Pjjj6LzpE9IYjV4nsfevXuRkZGBCxcuoGHDhoWucapbBd49m6PG7DdR56t34Vg7wPic3NMFXt0iIFGytVYJIcRarFixAhMmTEDXrl2Rnp5u3OFUrVZjyZIlovOl4E+shoODAxYsWAAAuHjxomUrQwipHGy85b9s2TKsWbMGU6dOhVT673LXERERxl1PxaDgT6zKpEmT0KlTJ0RHR2POnDmWrg4hxMoxjfRnHRNUAe7du4cmTZoUOu/g4ICcnBzR+VLwJ1aF4zgsXLgQHh4emDZtmnHnPkIIKZKNt/yrV69eZE/o/v37Ua9ePdH50mh/YnVCQ0OhVqsRGxsLDw8PS1eHEEIsZsKECRg1ahQ0Gg0EQcCZM2fwww8/YN68efj6669F50vBn1ilIUOGYPz48di9ezegtHRtCCFWy0bX9n9q2LBhUKlUmDZtGnJzc/Hmm28iICAAS5cuRf/+/UXnS8GfWKVx48Zh165d6NOnD9Q9W8Przc6i8xJk4v+6JRrLztNn/WCSZ7Dd2dM5s1VAmyR+nj0AaAW2ef7ns8SXHxEcx1S22kHDlN5dkcuU3sB4V9fAMM/fUIEB1VZ39XvWgAEDMGDAAOTm5iI7Oxs+Pj7MedI9f2K19u/fj2nTpiF99zEkfrENKRv2QZ/BtnAKIYRUJvfu3cOtW7cAAI6OjsbAf+vWLdy/f190vhT8idVSqVSYNWsWHKoHIPf8TWQdOY+UNXssXS1CiDV5usIfy2HFhgwZgpMnTxY6f/r0aQwZMkR0vtTtT6yaVCpFlfnvAoKA9D3HkbbzNwiCAI6z7j9YQkgFsfF7/hcuXECrVq0KnX/xxRcxevRo0flSy59YPY7jwEkkkHm5QdBokX3iMm3XSwixCxzHISur8D4TGRkZxtX+xKDgTyqN7FNXAQBJX2xD6tqfoUt+gpyLt6BLzbBwzQghlmLri/y0adMG8+bNMwn0BoMB8+bNw0svvSQ6X+r2J5WCIVeDnD+vAQDkgV7IOPQnMg6eAQBInFUI+GgQwHGQqBygCPB6XlaEEFti493+CxYsQJs2bVCnTh20bt0aAHDs2DFkZmbi8OHDovOl4E8qBcOTgm4vp4i68JvYH7qkJ9AlpEDq7oqUr3/Cg49WGa9169oC3kO6WqqqhBBiNvXr18elS5fw5Zdf4q+//oJKpcKgQYMwevRopkXQKPgTq3erz7SC/4xbWuTzOe99ji5duuD48eMAgJDEfJx7mgZAtZWfiy5bqmUcWMjYquB4y6aX5bG9fiFfWvJFzyFlmyoPIU38PP9rTmxzqRt4JzKlV0m1TOl5hnn6AKAR5Axpxd+LLjPWrnsrb/kDQEBAAObOnWvWPCn4k0rPyckJP//8M9RqNd588020aNECt2/fRs2aNS1dNUJIebPxbv+ncnNzERcXB63W9Eth48aNReVHwZ/YhCNHjgAAvv/+e3z//fcICgrCxx9/XORuWIQQG2LjwT8lJQXR0dH45Zdfinxe7Ih/Gu1PbEK3bt2wZcsWbNmyBceOHQPHcYiOjkZ4eDg0t+5aunqEVEo0pdbyxo0bh/T0dJw+fRoqlQr79+/Hxo0bUatWLezZI37RM2r5E5sgl8vRt29f4+PY2Fjk5uaifv36SPz5ILwG94fMQ225ChJSyfyyJR0pj3QYNM7b0lV5Lltf2//w4cP48ccfERERAYlEguDgYHTq1Amurq6YN28eunXrJipfavkTm+Xo6IiVK1ci//Y9PFqwFPp0Wg+AkNIKb+2EN/5H02YtLScnx7iev7u7O1JSUgAAjRo1wvnz50XnS8Gf2LQuXbog8OMPwEmkSFm1EQLPOPydEDvhEyCHXEHLaFtanTp1cOPGDQBAaGgoVq1ahYcPH2LlypXw9/cXnS91+xObJ/P0gPvrPZD69bcwpGdS9z8htsTGB/yNHTsWjx49AgDMnDkTXbp0wXfffQeFQoENGzaIzpeCP7F9AsBnFWwFzMlkZfpj5xinK7Oml+gYy2fs6JCybSkP3oEtvYyxfHmO+LRP3N2YytZ6pjKlZ5VlUDKlTzG4ik6by7DmfFnZ4j3/zMxMuLoW/PwHDhxoPB8eHo7Y2Fhcv34dVatWhZeX+Nsy1O1PbJ4hIxPpPx2AU/NwSF2cLV0dQgh5Lnd3dyQnJwMA2rdvj/T0dONzjo6OaNq0KVPgByj4EzuQsf8wIJHAvXcPS1eFEFIeBIbDCjk7O+Px48cAgKNHj0KnY+wCLAJ1+xObZ8jKgqJKAKTOTpauCiHE3Gzwnn/Hjh0RGRmJevXqAQB69eoFhUJR5LViN/eh4E9sntTVFZqbd8BrNOCkUnBy8WuWE0JIefv222+xceNG3LlzB7/99hsaNGgAR0dHs5ZBwZ/YPNeObZB94g88nD4PfE4uPPr3gkublpauFiHEDGxxwJ9KpcK7774LADh79iwWLFgAtVpt1jLonj+xeTIPd7h0aAs+p2DoeNrmXcj89TcL14oQYhYs9/ut+L4/AOh0OsTFxRmn+pkTtfyJ7XMyQN23PRQ1/SD39UTS/DXIOX8Brq++VHLaVLYtaeXZTMlhKPo2X6lJ89nSS9h2lQXPOE6JtXypRvwnu+MDtrZRZkO2eY55xdzjLa0Uxi19WWjyzT9ArTi22PJ/Si6XQ6Nh3Ne6GNTyJ3aBk0rh9EIjKKoGQBEcCE5O33sJIdZv1KhRWLBgAfR6vVnzpU9AYnecWjXB4zXbkH8nHg4hQZauDiGEhQ2O9n/Wn3/+iZiYGBw8eBCNGjWCk5PprKWdO3eKypeCP7E7ElXBymfZJ85R8CeksrPx4K9Wq9G7d2+z50vBn9gVgeeR8sUmAIA+MRV8nsb4ZYAQQqzN+vXryyVfuudP7AonkcB3ynAAgObqbeRdumHhGhFCWDwd8Mdy2CNq+RO7k3noJCCXwa1HJBybNrB0dQghLGy827969erguOK3Vr57966ofCn4E7vCa3XIO/83nFo2QfbvZ5F37m+o+3aBQ+1qkCho5T9CiHUZN26cyWOdTocLFy5g//79mDRpkuh8KfgTm3d/0AfG//M8D5ex85Bz4jwAwJD6BMkL12L8+PFYtGgR9Ho90tLS4OPjAwCo/elitsJZt9RlnOJr6UYNa/1lDPP0AbYtjZVpTEUjQ6NiSp+jYpvnn6FjKz+PF/9lWKthXKChLGy85T927Ngizy9fvhxnz54VnS/d8yd2RSKRYPPmzVizZg3y8/Oh1Wrh6+uLpKQkAED//v0RFBSEv/76y8I1JYSUhr3e83/55ZexY8cO0emp5U/sTo8eplv7duvWDevWrUP9+vWNf0xff/01li1bZonqEUJIibZv3w4PDw/R6Sn4E7u3evVqHD58GNOmTTOeM/cmGoSQcmLj3f5NmjQxGfAnCAISExORkpKCr776SnS+FPyJ3ZNKpTh9+jQmT56MjRs3YuDAgZg5c2aF1yPr/jU4qH2gUHtWeNmEVFaWWtt/+fLlWLhwIRITExEaGoply5ahWbNmRV67YcMGREdHm5xzcHAo1br9PXv2NHkskUjg7e2Ndu3aoW7duuIqDwr+hAAAfHx8cPbsWURFRWHDhg2QStk29CkrQRAQu2sNAMC/fW94NHoRnKRi60Aqj/wMDW5svYrGw8MtXRXLs0DLf8uWLZgwYQJWrlyJ5s2bY8mSJYiKisKNGzeMg4X/y9XVFTdu/LuuyPOm7z2rvBoiNOCPkH84OjrC1dW1wgM/ABg0ucb/Pzq8A/G/fFfhdSCVh4ObEn4RAZauht1atGgRhg8fjujoaNSvXx8rV66Eo6Mj1q1bV2wajuPg5+dnPHx9fZ9bhl6vR36+6bacSUlJmD17NiZPnozjx48zvQYK/oT844033sCuXbsQFxdX8YULps0PpZd/xdeBVCo+Teg9AuDflj/LASAzM9Pk+G/gfUqr1eLcuXPo2LGj8ZxEIkHHjh1x6tSpYquZnZ2N4OBgBAUF4dVXX8XVq1ef+7KGDx+OMWPGGB9nZWXhhRdewPLly3HgwAFERkZi3759z83jeajbn5B/DBs2DPPnz8fYsWOxa9cuAICkgrYllzk6o97/5kKfmwldZjqcgmpC4A3QxsfD0Te41F2E/8UZ2OrFuiU8yzx7S6dnXWMgM8+BKX22M1v6XD3bolUShh+ejvF9VxbcPwdLegAICjLd5GvmzJmYNWtWoetTU1NhMBgKtdx9fX1x/fr1IsuoU6cO1q1bh8aNGyMjIwOff/45WrZsiatXr6JKlSpFpjlx4gS+/PJL4+NvvvkGBoMBt27dgpubGz744AMsXLgQXbt2Lf2LfQYFf0L+ceDAAQAo8Rt5eZE6KCGRK5B+7Tzu71xpPF/3jSlQuhd9H5EQYh7x8fFwdXU1PnZwYPvy9awWLVqgRYsWxsctW7ZEvXr1sGrVKnzyySdFpnn48CFq1aplfBwTE4PevXvDzc0NADB48GCmTX+o258QAHFxcejXrx/q16+PDRs2WKwevF6HlNMHjY+lDo4U+Al5HjN1+7u6upocxQV/Ly8vSKVS48JgTyUlJcHPz69UVZbL5WjSpAlu375d7DVKpRJ5eXnGx3/88QeaN29u8nx2dnapyisKBX9CUBD8eZ7HokWL0LJlS4vVQ6pwQN13P4FHaCs4BlRHzVdHWqwuhFQGFb3Cn0KhQHh4OGJiYozneJ5HTEyMSev+eQwGAy5fvgx//+LHbYSFhWHTpoLtx48dO4akpCS0b9/e+PydO3cQECB+0Cd1+xMCoGnTpvD09MTy5cvx9ddfW7QuMpUTAtr3Lvh/0WOOCCEWNGHCBAwePBgRERFo1qwZlixZgpycHONc/kGDBiEwMBDz5s0DAHz88cd48cUXUbNmTaSnp2PhwoWIjY3FsGHDii1jxowZePnll7F161Y8evQIQ4YMMfmysGvXLrRq1Ur0a6DgTwgKpvl9/PHHGDVqFDp27Ij+/ftbukqEGOlztZA5sm30Y7MsMM+/X79+SElJwYwZM5CYmIiwsDDs37/fOAgwLi4OEsm/HetPnjzB8OHDkZiYCHd3d4SHh+PkyZOoX79+sWW0bdsW586dw8GDB+Hn54c+ffqYPB8WFlbsokKlwQmCUOJLz8zMhJubGzIyMkwGRBBiSwRBwKuvvorr16/j5s2bAIC6M9l29ZNnsdWJteVf2Uf7M+/qx/L6WYaQA8jsxfbLr+2dAgDg9QbcWPcnQvqHQeGqLHV61tH+Xsoc0Wl1OVrs6LixXGPG07jUYMRcSBWl/7n8l0GrwdVVH9ldfKN7/oT8g+M4BAQEQKGgFhaxDH16NjKPXwav1UPgC774SGRSVH+tEeQu5ht9Tgh1+xPyjJs3b6JevXrGx6z70bNiXWdAkc3W9BYkbM1fPWO8Yu15kOaL7znQK9lee76m7C1vg84BvEqNh8t/RqZDHhpOfbngCbUaeWXsxeAFtvpnasW3pnXaimtXWmpt/8qOgj8hz6hRowZ+//13CIIgemEdQsSSujhC1aAGpK5OCA7IsHR1Kgcb39WvvFC3PyHPGDJkCG7duoXvvqO19YnlKIJ84VjF3dLVqBQqeqpfRTIYDPj999+Rnp5u9rwp+BPyjJdeeglvvPEGxo8fj8ePH1u6OoQQOyaVStG5c2c8efLE7HlT8CfkPxYtWgSdToe5c+dauiqEkJKYaYU/a9WwYUPcvXvX7PlS8CfkP/z8/PDKK6/g5MmTotLnPriLx2eOQhAY57kRQkpky93+APDpp5/i/fffx88//4xHjx4V2n1QLBrwR0gR/Pz8cPLkSZR1sDqv1+H+9wU7cTkGhcDJK6iEFIQQUrynu/a98sorJoOQnw5KNhjELWZBwZ+QIoSEhCA2NhY1crIgc3Ipdbr8lATj/+Vqj/KoGiHkWTY+2v/IkSPlki8Ff0KK8Prrr2PKlClIObIXQR1Lv9TvkzPHjP/P+PMkfFp0YqoH8wp7jCv8SfPZbl1ItIzrBKjY0rO8ftbVCQ25bB+vrPP0DTzbmycl31l82bkVuCmFjQf/tm3blku+dM+fkCJ4enpi3rx5ePL3GeQklH6wTfaD2/CO6AB13Qhk3L4EADBocpFx+xJi96zDg4Oboc8Vvw0nIcT+HDt2DAMHDkTLli3x8OFDAMCmTZtw/Phx0XlS8CekGFFRUQCAx5dLN/BPEAToc7Igd1YDggCJXI7MO1dxbc1sxP20AVmxN/Hk6hnc372mHGtNiH2x9QF/O3bsQFRUFFQqFc6fP4/8/IJelYyMDKYZSRT8CSnGrl27AAASeemG/XEcB6WXH9Ku/oHcR/eh9PBD/IHv4Vy1NupET0XD9+ajardByEuKhyY1oeQMCSEls/Gpfp9++ilWrlyJNWvWQC7/d8noVq1a4fz586LzpeBPSDGGDh0KdZ1wpN84j/z0lFKlcfKvDk3KQ2gzH0PpHQg+Pw++L3aGQu0JAHANaQSZkytSL4jrros/tgMX10xCwumfRaUnhFQuN27cQJs2bQqdd3NzY1r5j4I/IcVwc3NDQNuekCodcW/P1yjF7tdwrdEQSi9/+L/0CvIfP4LM0QVK7wDj85xUCq+mbfHk6hlk3rlSqjyfpcvLhMAbkHTxMHKSY8v8mgixNZwgMB/WzM/PD7dv3y50/vjx46hRo4bofCn4E/IcUgcVpEpH8FoNUIoPCZfguqj95iSo6zTFk+vn4N7oRXASqck1Xk3bwDm4NmL3rMOjo7vLVB8n32rG/z84vhP6/NwypSfE5th4t//w4cMxduxYnD59GhzHISEhAd999x3ef/99jBw5UnS+NNWPkOcw5OdBk/oIEHgkntwL76btIHMsed5/0ukD4CQSeDUp3F3HSaSo1nMY4n/5DtmxN0pdl7zHCUj44ye4BNaGukZjPDy1B7Ex3yKk6ztlek2E2BJb39J3ypQp4HkeHTp0QG5uLtq0aQMHBwe8//77eO+990TnS8GfkOe4umoaLo7sjnXr1uHbb78Fn/sIZy5ehIODw3O3/R0w4CwOJt7C75+8Cy8vryKv2dTUB4MGDUIn7gE+//zzErcQnjZtGuYAMDx5gEl9RmPMse0IcAJ2f9QbEokEgYGBhfJo2e//RL3upyR6y34ySvSMGTBMlZdp2F67PI3t41VTVV7yRc+hkLL98ORS8YskcAxpiSmO4zB16lRMmjQJt2/fRnZ2NurXrw9nZ/HrMADU7U9IicLCwvDFF1/g999/x/Xr11GvXj2MGTMGKpUK69evLzKNh4cHUlNTsWDBgmLzHThwICIiIrBo0SKcO3euxHpMmTIFderUQW5uLj744ANUrVoV169fR3BwMIKCgtClSxc8ePAAeXl5ol8rIZWOjXf7v/3228jKyoJCoUD9+vXRrFkzODs7IycnB2+//bbofCn4E1JKDRs2xLx583Dv3j2sWLEC+fn5ePvtt3HixAmsXbsWaWlpxmufTsl5+eWXi82P4zg0a9YMAODv719i+c7OzlixYgVeeuklvP3224iPj8esWbOwZMkSAMDBgwcRFBSEzz77jOFVElK52Po8/40bNxb5hT4vLw/ffPON6Hwp+BNSBlOmTMHIkSOh1xd0qcrlcqxatQrDhg1DaGiocfWtfv36geO4EtflnjZtGry9vfHCCy9g4sSJOHjwIDQaTbHXR0ZG4tixY2jfvj0EQcC7775b6L7fhQsXGF8lIcTSMjMzkZGRAUEQkJWVZbKT35MnT7Bv3z74+PiIzp+CPyFl5OjoaPx///79sXnzZowfPx4cx+HVV1+FVqtF8+bNMWPGDMyfPx8JCcUv6OPv74/z58+jW7du2LJlC6KiouDu7o533nnnudt13rhxA2q1Gj4+PpBIJPj888+Nz/34449MW30SUqnYaLe/Wq2Gh4cHOI5D7dq14e7ubjy8vLzw9ttvY9SoUaLzp+BPSBldvXrV+P/ff/8dfn5+mD59Onbv3o2//voLX331FQBg/PjxUCqVxsfFqVKlCtasWYP4+HhcvnwZ06dPx+bNm9GkSRNs3rzZuJznszQaDRQKhfHxxIkTcf36dXz00UdwcnJCixYtMH36dGjz6EsAsW222u1/5MgRxMTEQBAEbN++HYcPHzYex48fR1xcHKZOnSo6fwr+hJTBsmXLsH//fiiVSkilUnh6euLYsWNwd3dH06ZN0atXLyxatAg3b96Em5sb3n77baxcubJUg/A4jkPDhg3x0Ucf4cKFC6hRowbeeOMNVKlSBRs3bjS5NiwsDMnJycbbDABQp04dzJkzBwcPHkTDhg3x6aef4v7FH83+MyCElL+2bduiXbt2uHfvHl599VW0bdvWeLRo0QIBAQElZ/IcFPwJKSWtVosxY8YAAGQyGWbMmIFTp04hODjYeM28efPg6OiIZs2a4eeff8aYMWOQlpaGb7/9tkxlhYSE4NChQ7h27Rq6dOmCIUOGmOTRsmVLODo6onv37iarf+3btw8//fQT3n33XUgkEkikiqKyJ8R22Gi3/1PBwcGQSCTIzc3F9evXcenSJZNDLE4oxfqimZmZcHNzQ0ZGBlxdXUUXRkhlJggCVq9eDYVCgR49ehQ7fz8jIwODBg3Cnj17MHv2bJw7dw63b9/GlStXSpzLX1y5w4YNw7p16xAVFYXOnTujS5cu0Ov1CA0NxaBBg7Bx40bk5OTA3d0dOp0OANCkSRNkSmvBMygU0lJuTvRfsjy2Te15Odue9HolW3qOofqsawyk12RrWzm0fsyU3tmh8O2isnCSa0Wn1efk49euq8o1ZjyNS+F950AmV4rOR6/T4NzWqVYb31JSUhAdHY1ffvmlyOcNBnFrKlDLn5BS4jgOI0aMQHR0dLGBHyjYE2DXrl2YPXs2Zs6cCV9fX/z99984dOiQ6HK//vprrF+/HlqtFlOnTkWDBg2wZs0a+Pj4GD+wHBwcEBISAgCoV68e2rVrhztntyL28l5R5RJCLG/cuHFIT0/H6dOnoVKpsH//fmzcuBG1atXCnj17ROdLwZ+QciCRSDBjxgy0bt0aCQkJqFevHjZv3iw6P47jMGTIEBw+fBjp6elYunQpVq1aheTkZKxduxZ5eXmQyWTGjYIePnyIuLg4AEBW6n1zvCRCrJMgsB9W7PDhw1i0aBEiIiIgkUgQHByMgQMH4rPPPsO8efNE50vBn5ByNGDAAOzbtw8uLi749ddfy7yLX1EcHBwwZswY4xoCeXl5xsWEDh06hE2bNiEzMxPXr18HAEikbMvEEmLNbHW0/1M5OTnG+fzu7u5ISSnYXrxRo0Y4f/686Hwp+BNSjoYNG4bmzZvjzJkziI+PL3JrTrFatWqF+/fvAwAePHgAQRAQFBSEunXrAvh3SqLSydNsZRJidWx8wF+dOnVw40bBBmChoaFYtWoVHj58iJUrV5ZqZdDi0MY+hJQjqVSK3bt3IyIiAg8ePMDRo0dRq1Yts+UfHBwMnU4HrVZrHEwYGhqKgQMHguM47PzpMFLjzsO7egTUvrXNVi4hpGKMHTsWjx49AgDMnDkTXbp0wXfffQeFQoENGzaIzpeCPyHlzNfXF9evX8e3336LTp06mT1/mUwGmezfP2W5XI5NmzYBAI6H98a98zsRe/EnuHWeIGq2ASHWjOPZZnWwpC1P9+7dQ/Xq1TFw4EDjufDwcMTGxuL69euoWrXqcwcel4SCPyEVwMnJCSNGjKjwcv1qtoTSxQvXfluNjORb1Pontoe1695Ku/1DQkIQHByMyMhItG/fHu3atUOVKlXg6OiIpk2bMudPwZ8QG8bLObgE1gLHSZCbnQzXKnXKlgHj7sASPdsnq0THOM+foXiBsZNEIn6aPAAgL9+yAzX9HbNEp9UxrBFAChw+fBhHjx7F0aNH8cMPP0Cr1aJGjRpo3749IiMjERkZCV9fX9H5U/AnxMblPH4AQeDh6C5+cBAh1op1xL61jvZv164d2rVrB6BgL4+TJ08avwxs3LgROp0OdevWNdlrpCwo+BNi4x7HXYRc6QIX7xqWrgoh5sc6V9/K5/kDgFKpRPv27fHSSy8hMjISv/zyC1atWmWczisGBX9CbFxe+iM4e1cDJ6GZvYRUJlqtFn/88QeOHDmCo0eP4vTp0wgKCkKbNm3w5Zdfom3btqLzpuBPiI3TZKbCPaiRpatBSLmw1W7/9u3b4/Tp06hevTratm2LESNG4Pvvv2ea2/8sCv6E2LD87DTk56TBxae6patCSPmw0dH+x44dg7+/v3Gkf9u2beHpab4Fu6gfkBAblpeRBABwcg+0cE0IIWWRnp6O1atXw9HREQsWLEBAQAAaNWqE0aNHY/v27cZlfsWilj8hNiw/5wnAcVA4ulm6KoSUC1vt9ndyckKXLl3QpUsXAEBWVhaOHz+OI0eO4LPPPsOAAQNQq1YtXLlyRVT+FPwJsUG3bt3CJ598gvt/7oDCSQ1BIRXVu8kZGD8ZGZPLYLnl13gZ20R/vRNb+TqdlCm9gWfr2HXzEb/Ig1ZWgfP87WC0P1DwZcDDwwMeHh5wd3eHTCbDtWvXROdHwZ8QG7Rv3z7jEr++9dpYuDaElB9bbfnzPI+zZ8/i6NGjOHLkCE6cOIGcnBwEBgYiMjISy5cvR2RkpOj8KfgTYoPeffddnD59Gj/88AMcPel+PyGVjVqtRk5ODvz8/BAZGYnFixejXbt2CAkJMUv+FPwJsUEODg4YOHAgfvjhB0ikll0mlpByZaOj/RcuXIjIyEjUrl0++3FQ8CfERoWGhkIilSMr6Q6cvYMtXR1CyoWtdvuX90ZgNNWPEBsVGBgIqYMj9PmMu/MQQmwOtfwJsVGJiYnQ5WbAie75E1vGCwUHS3o7RMGfEBt14sQJAICzN63uR2yYjd7zL28U/AmxUX/++SeqVKmCi9tmi86jbdfPmOogzWebp8/zbHPtBQlDesZ5/jrGef4C4zx9A+P8dZVU/Fx9iVTHVDYpfxT8CbFRcrkcer0e+fn5cHBwsHR1CCkXHBgH/JmtJpULDfgjxEa1bdsWKSkpqF27Nm7cuGHp6hBSPp6u8Mdy2CEK/oTYqI4dO+Kbb75BXFwcHjx4YOnqEEKsCAV/QmxYgwYNAABRUVG4c+eOhWtDiPk9nefPctgjCv6E2LDQ0FB069YNBoMBb7zxhqWrQ4j5CWY47BAFf0Js3I8//oiIiAioVCpLV4UQs+MEgfkQY/ny5ahWrRqUSiWaN2+OM2fOlCrd5s2bwXEcevbsKapcc6HgT4gNu3btGjp37oxz585h4sSJlq4OITZhy5YtmDBhAmbOnInz588jNDQUUVFRSE5Ofm66+/fv4/3330fr1q0rqKbFo6l+hNiwqIEfIP78YdRsPQhztt3GnG2LypRexbj6GWdgm+cvZZznr3MV/xGX78bWNuIVjD87xpvRrOnzDArRabUGpqLLhv/nYElfRosWLcLw4cMRHR0NAFi5ciX27t2LdevWYcqUKUWmMRgMGDBgAGbPno1jx44hPT2dodLsqOVPiA1z8akBAJCrXC1cE0LKR0V3+2u1Wpw7dw4dO3Y0npNIJOjYsSNOnTpVbLqPP/4YPj4+GDp0qOjXak7U8ifEhjl5VIFUoUJm4k24+NAyv4QUJzMz0+Sxg4NDkYtjpaamwmAwwNfX1+S8r68vrl+/XmTex48fx9q1a3Hx4kWz1ZcVtfwJsWGcRAJX35rIeHQTgp0uZkJsnJlG+wcFBcHNzc14zJs3zyzVy8rKwltvvYU1a9bAy8vLLHmaA7X8CbFxHtXCcOfYJsSe2YHgZq+B4wp/59fn50KqUAJAkc8TYrVYV+n7J218fDxcXf+9PVbcktheXl6QSqVISkoyOZ+UlAQ/P79C19+5cwf3799Hjx49jOd4vmCggUwmw40bNxASEiK+/iJR8CfExnkGh4HX5ePeH1shUzqjSmgXk+dz0h7g6r7FkMgUEHgD6nYcSbcIiN1xdXU1Cf7FUSgUCA8PR0xMjHG6Hs/ziImJwejRowtdX7duXVy+fNnk3LRp05CVlYWlS5ciKCjILPUvKwr+hNgB75rNodNk48HFfXD2CoY6sB4AIP3B37h/ZjsAwM2/Dp7EX8bt3zfCvWojuAXUhcqhNgAgL/cxDPp8OLsGWOw1EFIU1lX6xKSdMGECBg8ejIiICDRr1gxLlixBTk6OcfT/oEGDEBgYiHnz5kGpVKJhw4Ym6dVqNQAUOl+RKPgTYuMyEm7gxuHVxsfZKfeNwf/Rtd+gzc1A1YhX4Ve3DTITbyPlzhlkJNxA8s2TSHavDk+ferh7Yx/kcie0bD/dUi+DkKKZqdu/LPr164eUlBTMmDEDiYmJCAsLw/79+42DAOPi4iCRWPftM04oxSigzMxMuLm5ISMjo1TdIoQQ63HkyBG0b9/e+DgtLQ1yuRxXrlyBp6cnIiMjERQUhKNHjxrvcwqCgBUrVmDUqFHGdC6uVdC0ReFuzeeRZ+uZ6s4xThjP9xW/qmFmVba2UUYdtjUOOF8NU3onx3ym9G0Dxe8Foc3WYXXbbeUaM57GpbYtpkEmU4rOR6/X4LdTn9pdfLPuryaEEGaRkZEQBAHHjx8HAHz44YcICwtDixYt0KlTJ7z11luFVgDkOA4jR45E1Rrt4eZecP/fw7uORepPyPNwPPthjyj4E2InWrZsiZYtW2LVqlXw8PDAzp07UbNmTcyfPx9AwVrlW7ZsMV7PcRyq1+qMeo37Ayho+RNidZ52+7Mcdoju+RNiJziOw549e/Do0SM0aNAAHMehV69eyM7ORvPmzZGamophw4bBy8sLHTp0+DedRAoA4Hm2LnxCygXrznz2Gfup5U+IPfH09ETDhg3Bcf+ume/s7IxPP/0Ujx8/Rl5eHjp16oQVK1YYn5fLnaBUueNJ2m1LVJkQUg4o+BNC0KtXLyQkJODll18GAIwaNQo7duwAUNBj4BvQFIkPzyE1+SqtFEisiqW29K3sKPgTQgAAPj4+2LVrF9566y0IgoA33ngD2VmJAICgam3h7hGCqxc24d6tAxauKSHPoHv+olDwJ4QYyWQyrF+/Hs2bN4dEIkHc3cMAAKlMgUbh0QgMfgmP4k9DEOx0iDQhNoIG/BFCTEgkEsyYMQPdunVDWsoN6DU5kCucAADu7jXwMPY4dHnZUDi4mIwdKBLP1qqSaNkGGcoY1hngDGwfj7JstrZVvpOcKT0Y5/ln6sXPndfqK7BdKQBg+S5qnw1/avkTQgqLjIw0/v/C2ZXQ6XIhCDwexv9hPHftypbikhNSYeievzjU8ieEFKJSqfDSSy/h3Pkb0OZn4cRvnwL/dPUHBrXEw/iT0OSloV7DvrQLICGVEP3VEkKKtHTpUmg0TyBXuMLTszaq1eiIBqFvISi4jfGauHtHLVdBQoB/5vmzDPiz9AuwDAr+hJAiNW3aFKFN3oaD0hVpj2/i/t1fcfWvTcjIiIWPbygA4N6dg8jMeGDhmhK7RqP9RaFuf0JIsdQeNRDmUQN6fT6yMh/g+tVtuHb5B3h5N4RMpoJen4ekR+fh6kZL/xJSmVDLnxBSIpnMAe4eIWgYNgj+gc3wJO2mcQaAwcA2qpwQJrwZDjtELX9CSKm5uASgTv3XoFS5497tA/D1a4LgGh1KTkhIOWEdsU+j/Qkh5D+OHPygyPM8Pwljx47Fl19+ifHj+uGDD4q+rtNLc9gqoDMwJZc/zhGd1jGZbZ69QSllSi/RsZWfwTkzpX/g7CY6rT63AnuDWO/bU/AnhJDSkUgk+OKLL+Dh4YEpU6ZAoVBg/Pjxlq4WIaSUKPgTQkThOA6zZ89GXl4e3n//feh0OiQlJaFdu3bo3r27cfW/rJxHuHJzG3JyU+Co9EDdkFfgoQ6xcO2JzaCWvyg04I8QwmTOnDmoUqUKPvjgA6xbtw6vvPIKevTogdTUVABAYvJfyMlNhodbDTgoXHHx2re4df8AdPo85GuzLVx7UunRVD9RKPgTQpjI5XLs3LkTkydPRkJCAn766Sf8+uuviIyMxJOMe/DzCYPatRrSMu5ApfSAwBsQ+/AYjp/9HMf+nI/M7ARLvwRC7A51+xNCmIWHhyM8PBwA0L17d4SEhCAhIQFpaWsBABKJHCqlOxKSzxnTPJ0iqNOJH5RHCHgAJewvVWJ6O0TBnxBidhzHIS0tDQAQ4BsOiUSOB48KNgVyda4ChdwZHuoQ3Lr3C/Lyn1iyqqSSo6l+4lC3PyHE7D799FMAgErpiXohr6Juje6oV7MXAMDBwRVh9QeiakALODv5IS39riWrSohdopY/IcTsevbsidzcXOh0Ori6uhrP5+TkQBAEODsXzEH//HMHTJ06FQu/7IawsDA8evQIKSkpaNy4MQCgS9gMpnpwORrRaZ3vZjKVLTG4MKXPzWJbJ0CWw7ZOQKyzh+i0fK74n3uZ0Wh/UajlTwgpFyqVyiTwA4CTk5Mx8APA6NGjUb9+fURGRmL16tUICAhAaGgoeN5Ob8SSsuMF9sMOUfAnhFiMUqnE0aNHERkZiREjRhjPx8bGWrBWhNg+Cv6EEItyc3PDzp07kZSUhMuXLyMgIAAvvvgirl69aumqkcqA5vmLQsGfEGIVfHx80LBhQ5w7dw5SqRSrV6+2dJVIpcAa+Cn4E0KIxfn5+aFHjx748ccfIdhpq4yUAbX8RaHgTwixOgMHDkRsbCzSc+ItXRVCbBJN9SOEWJ1WrVohODgYCRmXoVZXE50PZxA/a4BLTRedFgCctHqm9Ip0ti15DfFsUwWTpU7iy85nK7tMeMauexrtTwgh1kEikWDAgAFIfPI3eJ4tiBIbJ/Dshx2i4E8IsUoDBgyA3qBBauZtS1eFEJtDwZ8QYpXq168PF5UfEh5fsnRViDWjAX+iUPAnhFgtf8/GSMm4Ca0+19JVIdaKVvgThYI/IcRqBXiGQsJJcS/xuKWrQohNoeBPCLFaCpkjqvm2QHzyGWi0GZauDrFG1O0vCgV/QohVC/ZtAanUAXcSfrN0VYg1EsAY/C39AiyD5vkTQqyWIJdCKndEjcC2uBG7Hz7eDeGlrlX6DHiD+LJz80SnBQDksI1TkGt1bOn1bFMkA3K9RKfV6zW4wVQ6KW/U8ieEWL0qvi/Aw606LlzfhMTUy5auDrEm1O0vCgV/QojVk0hkaFp3EHw8GuD6/b3Q6nIsXSViLXie/bBDFPwJIZUCx0lQt1o3CIKA6/f30aY/pAC1/EWh4E8IqTQcFM6oV707kh5fxrV7e2jpX0JEogF/hJBKxc+rEQy8Dtfu/YSMrHg0qPkaXJ0CLF0tYimsrXdq+RNCSOUQ6NMUzRuOADgJzlxZjTvxh6kXwF7RCn+iUPAnhFRKLk5+aN7wHVQPaIN7D3/HmSurkZWbZOlqFUkQBCTr4pCkvW/pqhACgLr9CSFW7OCp6aW67ty5cxg8eDDOXVuNqVOnonv37qhduzb61p4iumwhP190WgDgNQXpc4RMXBfO4wmSAQAdNX1LlV7KcUzlC7ls6wxIM7LEly1omcouW1k8BIZteVnSVmbU8ieEVHrh4eE4d+4cJk6ciE8++QQRERHw8vLCuSc/42HedYt9wCcKcfhTiEE+cuEJP3CQ0CwFcxMYu/zt9PdBwZ8QYhMcHBwwb948PHnyBGfOnMFnn30GPa/H5YzD+CNtF3L1mRVanwThHq4If8Ad3mjGdYI3FwBAAMfYoifEHCj4E0JsiouLC1544QWMHTsWzT17orlHL2j5PJx98hO0POOSvaWUZkjENeEsAlEDjblWkHFyGGCAhO60mh/N8xeFgj8hxKa5K/zxgnsP6AUtrmaU/+ZA+UIuzuYfggAB/lw1Y0vfAD2kkJZ7+XaHVvgThYI/IcTmOcrcUMelBZLy7yJDl1zOpXHwlPhDBSdcEH5HhpAGADAIBgr+xGpQ8CeE2AV/ZW04SJzwKO92uZbjwKkQruyIF7koOMEVV4Q/YBD04GEARx+55kfd/qLQO5EQYhcknARucm9k6VMrpDwpJ0MDrjnykYe7wlU4ca7IQzZ0FTgNzh4IPM982CMafUIIsV3/+WB3kDgiXZdUqvu8fB7b4EBOJocz1KhmqIf7/N94QdoJgl5AqiQRAZLqJWdgMDCVz2vZvmRwDCvf8YKOqewyEQQAtLxvWVHLnxBiNzhU/DS7KpKa4MEjV8iGmvPGXcMVZAnpFV4PQp5FwZ8QQsqRA6eCAg7IFtLRQNocUkhxRn8QiXycpatmGyy0tv/y5ctRrVo1KJVKNG/eHGfOnCn22p07dyIiIgJqtRpOTk4ICwvDpk2bxL5is6DgTwixGxLIoOPzK3SVPb2ggxb5cOSc4ci5oJmsM7y5QFw2nECekFNh9bBZggAIPMNR9vfCli1bMGHCBMycORPnz59HaGgooqKikJxc9EwSDw8PTJ06FadOncKlS5cQHR2N6OhoHDhwgPXVi0bBnxBiN3wcgqHhs/FAc63Cykzg74IDB3fOF0DBQMCG0hcBACn8gwqrBzGfRYsWYfjw4YiOjkb9+vWxcuVKODo6Yt26dUVe365dO/Tq1Qv16tVDSEgIxo4di8aNG+P48eMVXPN/UfAnhNgND0UAgpT1cS3rJLL1T8q9vHwhD3f5K/DjqkHFORnPP+TvAgDcOK9yr4OtE3iB+SgLrVaLc+fOoWPHjsZzEokEHTt2xKlTp0quryAgJiYGN27cQJs2bcr8es2FRvsTQuxKXZcWSNMl4FJmDF507wUJVz4L7xgEAy4ZToADh9rSMJPn4vlb8OWqwk3iWS5l2xWBB8AwXe+fTZ8yM033fnBwcICDg0Ohy1NTU2EwGODr62ty3tfXF9evXy+2mIyMDAQGBiI/Px9SqRRfffUVOnXqJL7ejMoU/P/7wyGEEGum54ue7tbApS3OpO/B9exTqO3UrOi0DNPV9NDjb/05ZOAxmkjaQAKpMT+NkIMcZCCIq/XcMiQCW8esgXG6HccwLEKPCpzqZyZBQUEmj2fOnIlZs2aZLX8XFxdcvHgR2dnZiImJwYQJE1CjRg20a9fObGWURamCv0KhgJ+fX6EfDiGEVGZxeVcQl3elXMs4zx8tsmF6nT+L6/zZ4hNmlVuVSkfPltzPzw8KhcI8dXkOgRcgMHxTeTr4Mz4+Hq6ursbzRbX6AcDLywtSqRRJSUkm55OSkuDn51dsORKJBDVr1gQAhIWF4dq1a5g3b551B3+lUol79+5By7hoBCGEEPugUCigVCrLvyAzdfu7urqaBP/iKBQKhIeHIyYmBj179gQA8DyPmJgYjB49utTF8jyP/Px8UVU2h1J3+yuVyor5RRJCCCGlpIeOaYE/MbcoJkyYgMGDByMiIgLNmjXDkiVLkJOTg+joaADAoEGDEBgYiHnz5gEA5s2bh4iICISEhCA/Px/79u3Dpk2bsGLFCvEVZ0QD/gghhFQ6T29HH0/cx5xXWW9R9OvXDykpKZgxYwYSExMRFhaG/fv3GwcBxsXFQSL5d8xGTk4O/ve//+HBgwdQqVSoW7cuvv32W/Tr14+57mJxQkWudkEIIYSYiUajMcvt6Aq7RWFFKPgTQgghdoYW+SGEEELsDAV/QgghxM5Q8CeEEELsDAV/QgghxM5Q8CeEEELsDAV/QgghxM5Q8CeEEELsDAV/QgghxM5Q8CeEEELsDAV/QgghxM5Q8CeEEELszP8DA7s0BloLxZAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = xr.open_dataset(HERE / \"test_data\" / \"hycom.nc\")\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds[\"surf_el\"].isel(time=0).plot(ax=ax, transform=ccrs.PlateCarree())\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ocean_forcing = SCHISMDataOcean(\n",
    "                elev2D = SCHISMDataBoundary(\n",
    "                    id=\"hycom\",\n",
    "                    source=SourceFile(\n",
    "                        uri=HERE / \"test_data\" / \"hycom.nc\",\n",
    "                    ),\n",
    "                    variable=\"surf_el\",\n",
    "                    coords={\"t\": \"time\", \"y\": \"ylat\", \"x\": \"xlon\", \"z\": \"depth\"},\n",
    "                    interpolate_missing_coastal=True,\n",
    "                    ),\n",
    "                )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.schism.data:Fetching elev2D\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('schism_procedural/hgrid.gr3'),\n",
       " PosixPath('schism_procedural/albedo.gr3'),\n",
       " PosixPath('schism_procedural/diffmin.gr3'),\n",
       " PosixPath('schism_procedural/diffmax.gr3'),\n",
       " PosixPath('schism_procedural/watertype.gr3'),\n",
       " PosixPath('schism_procedural/windrot_geo2proj.gr3'),\n",
       " PosixPath('schism_procedural/drag.gr3'),\n",
       " PosixPath('schism_procedural/hgrid.ll'),\n",
       " PosixPath('schism_procedural/hgrid_WWM.gr3'),\n",
       " PosixPath('schism_procedural/vgrid.in'),\n",
       " PosixPath('schism_procedural/wwmbnd.gr3'),\n",
       " PosixPath('schism_procedural/tvd.prop'),\n",
       " PosixPath('schism_procedural/sflux'),\n",
       " PosixPath('schism_procedural/elev2D.th.nc')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_forcing.get(destdir=workdir, grid=grid)\n",
    "list(workdir.glob(\"*\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; ()&gt; Size: 8B\n",
       "array(&#x27;2023-01-02T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 8B 2023-01-02\n",
       "Attributes:\n",
       "    long_name:      Time\n",
       "    standard_name:  time\n",
       "    base_date:      [2023    1    2    0    0    0]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-59d562c0-a5ac-48f3-984d-5f0b9764e4fc' class='xr-array-in' type='checkbox' checked><label for='section-59d562c0-a5ac-48f3-984d-5f0b9764e4fc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2023-01-02</span></div><div class='xr-array-data'><pre>array(&#x27;2023-01-02T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-26985d97-48df-4c98-80c2-ac44f1b4abd9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26985d97-48df-4c98-80c2-ac44f1b4abd9' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-02</div><input id='attrs-7682af0b-68d7-4652-b53f-f066529fcc49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7682af0b-68d7-4652-b53f-f066529fcc49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cff86f19-7113-4823-a6a2-7c94da4fe600' class='xr-var-data-in' type='checkbox'><label for='data-cff86f19-7113-4823-a6a2-7c94da4fe600' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>base_date :</span></dt><dd>[2023    1    2    0    0    0]</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2023-01-02T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e9634e3-afa7-4268-af93-e166669de47c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e9634e3-afa7-4268-af93-e166669de47c' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-921e6674-9e27-48d4-b26e-41935e953e9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-921e6674-9e27-48d4-b26e-41935e953e9b' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>base_date :</span></dt><dd>[2023    1    2    0    0    0]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' ()> Size: 8B\n",
       "array('2023-01-02T00:00:00.000000000', dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 8B 2023-01-02\n",
       "Attributes:\n",
       "    long_name:      Time\n",
       "    standard_name:  time\n",
       "    base_date:      [2023    1    2    0    0    0]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsb = xr.open_dataset('schism_procedural/elev2D.th.nc')\n",
    "dsb.time[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<matplotlib.collections.PathCollection at 0x14a7799a2b00>"
      ]
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     "execution_count": 20,
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    {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin, vmax = 0.3, 0.9\n",
    "time = dsb.time[0]\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ds[\"surf_el\"].sel(time=time).plot(ax=ax, transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax)\n",
    "ax.coastlines()\n",
    "values = dsb.time_series.isel(time=0)\n",
    "x,y = grid.boundary_points()\n",
    "ax.scatter(x, y, transform=ccrs.PlateCarree(), c=values, cmap=\"viridis\", vmin=vmin, vmax=vmax, edgecolor=\"black\")\n",
    "\n",
    "# Check for nans (there shouldn't be any)\n",
    "# nans = dsb.time_series.isel(time=0).isnull().squeeze()\n",
    "# ax.scatter(x[nans], y[nans], transform=ccrs.PlateCarree(), c=\"red\", edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       ".xr-var-data,\n",
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       "\n",
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       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 14kB\n",
       "Dimensions:  (time: 3, ylat: 46, xlon: 25)\n",
       "Coordinates:\n",
       "  * ylat     (ylat) float64 368B -25.0 -24.8 -24.6 -24.4 ... -16.4 -16.2 -16.0\n",
       "  * xlon     (xlon) float64 200B 145.0 145.4 145.8 146.2 ... 153.8 154.2 154.6\n",
       "  * time     (time) datetime64[ns] 24B 2023-01-02 2023-01-03 2023-01-04\n",
       "Data variables:\n",
       "    surf_el  (time, ylat, xlon) float32 14kB ...\n",
       "Attributes:\n",
       "    classification_level:      UNCLASSIFIED\n",
       "    distribution_statement:    Approved for public release. Distribution unli...\n",
       "    downgrade_date:            not applicable\n",
       "    classification_authority:  not applicable\n",
       "    institution:               Fleet Numerical Meteorology and Oceanography C...\n",
       "    source:                    HYCOM archive file\n",
       "    history:                   archv2ncdf2d\n",
       "    comment:                   p-grid\n",
       "    field_type:                instantaneous\n",
       "    Conventions:               CF-1.6 NAVO_netcdf_v1.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d3334c93-93d6-4be3-b3cb-9af397a3776a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d3334c93-93d6-4be3-b3cb-9af397a3776a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>ylat</span>: 46</li><li><span class='xr-has-index'>xlon</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d8fa034c-0729-4ce5-889d-af7c473b8ed0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8fa034c-0729-4ce5-889d-af7c473b8ed0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ylat</span></div><div class='xr-var-dims'>(ylat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-25.0 -24.8 -24.6 ... -16.2 -16.0</div><input id='attrs-9652f302-ae14-45cc-b19b-8f70b18996f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9652f302-ae14-45cc-b19b-8f70b18996f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1489f038-ab19-4562-97b4-8aafda24d924' class='xr-var-data-in' type='checkbox'><label for='data-1489f038-ab19-4562-97b4-8aafda24d924' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>NAVO_code :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([-25.      , -24.799999, -24.6     , -24.4     , -24.200001, -24.      ,\n",
       "       -23.799999, -23.6     , -23.4     , -23.200001, -23.      , -22.799999,\n",
       "       -22.6     , -22.4     , -22.200001, -22.      , -21.799999, -21.6     ,\n",
       "       -21.4     , -21.200001, -21.      , -20.799999, -20.6     , -20.4     ,\n",
       "       -20.200001, -20.      , -19.799999, -19.6     , -19.4     , -19.200001,\n",
       "       -19.      , -18.799999, -18.6     , -18.4     , -18.200001, -18.      ,\n",
       "       -17.799999, -17.6     , -17.4     , -17.200001, -17.      , -16.799999,\n",
       "       -16.6     , -16.4     , -16.200001, -16.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xlon</span></div><div class='xr-var-dims'>(xlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>145.0 145.4 145.8 ... 154.2 154.6</div><input id='attrs-6bf6f66b-e53f-4099-b394-d5a28042f223' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bf6f66b-e53f-4099-b394-d5a28042f223' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c8c5947-9a97-45a2-a5f1-86fb6887c5d5' class='xr-var-data-in' type='checkbox'><label for='data-3c8c5947-9a97-45a2-a5f1-86fb6887c5d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>modulo :</span></dt><dd>360 degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>NAVO_code :</span></dt><dd>2</dd></dl></div><div class='xr-var-data'><pre>array([144.959961, 145.359985, 145.76001 , 146.160034, 146.560059, 146.959961,\n",
       "       147.359985, 147.76001 , 148.160034, 148.560059, 148.959961, 149.359985,\n",
       "       149.76001 , 150.160034, 150.560059, 150.959961, 151.359985, 151.76001 ,\n",
       "       152.160034, 152.560059, 152.959961, 153.359985, 153.76001 , 154.160034,\n",
       "       154.560059])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-01-02 2023-01-03 2023-01-04</div><input id='attrs-44ed07ec-3188-49f4-a7be-1236fe69d23e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44ed07ec-3188-49f4-a7be-1236fe69d23e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfe4025e-37c2-4e9a-9ba1-4645ceddd5c7' class='xr-var-data-in' type='checkbox'><label for='data-bfe4025e-37c2-4e9a-9ba1-4645ceddd5c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Valid Time</dd><dt><span>time_origin :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>NAVO_code :</span></dt><dd>13</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-01-02T00:00:00.000000000&#x27;, &#x27;2023-01-03T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-04T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aef03897-bdd7-4ecd-bd76-ac6a694e6b00' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aef03897-bdd7-4ecd-bd76-ac6a694e6b00' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>surf_el</span></div><div class='xr-var-dims'>(time, ylat, xlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26d6c861-0ace-4f16-9573-fbc25bb49109' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26d6c861-0ace-4f16-9573-fbc25bb49109' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da40eb0-dcbd-47d4-8d76-af97bc7a376c' class='xr-var-data-in' type='checkbox'><label for='data-8da40eb0-dcbd-47d4-8d76-af97bc7a376c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>time lat lon </dd><dt><span>long_name :</span></dt><dd>Water Surface Elevation</dd><dt><span>standard_name :</span></dt><dd>sea_surface_elevation</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>NAVO_code :</span></dt><dd>32</dd></dl></div><div class='xr-var-data'><pre>[3450 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2d4d3d6-6c39-4e29-9c1a-2dacfaaa3d33' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2d4d3d6-6c39-4e29-9c1a-2dacfaaa3d33' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ylat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3dce9719-7e98-400a-8998-41991ea6908a' class='xr-index-data-in' type='checkbox'/><label for='index-3dce9719-7e98-400a-8998-41991ea6908a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -25.0, -24.799999237060547, -24.600000381469727,\n",
       "       -24.399999618530273, -24.200000762939453,               -24.0,\n",
       "       -23.799999237060547, -23.600000381469727, -23.399999618530273,\n",
       "       -23.200000762939453,               -23.0, -22.799999237060547,\n",
       "       -22.600000381469727, -22.399999618530273, -22.200000762939453,\n",
       "                     -22.0, -21.799999237060547, -21.600000381469727,\n",
       "       -21.399999618530273, -21.200000762939453,               -21.0,\n",
       "       -20.799999237060547, -20.600000381469727, -20.399999618530273,\n",
       "       -20.200000762939453,               -20.0, -19.799999237060547,\n",
       "       -19.600000381469727, -19.399999618530273, -19.200000762939453,\n",
       "                     -19.0, -18.799999237060547, -18.600000381469727,\n",
       "       -18.399999618530273, -18.200000762939453,               -18.0,\n",
       "       -17.799999237060547, -17.600000381469727, -17.399999618530273,\n",
       "       -17.200000762939453,               -17.0, -16.799999237060547,\n",
       "       -16.600000381469727, -16.399999618530273, -16.200000762939453,\n",
       "                     -16.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;ylat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xlon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e37b3208-ea29-48ae-b94c-36040d74d34a' class='xr-index-data-in' type='checkbox'/><label for='index-e37b3208-ea29-48ae-b94c-36040d74d34a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   144.9599609375, 145.3599853515625,  145.760009765625,\n",
       "       146.1600341796875,   146.56005859375,    146.9599609375,\n",
       "       147.3599853515625,  147.760009765625, 148.1600341796875,\n",
       "         148.56005859375,    148.9599609375, 149.3599853515625,\n",
       "        149.760009765625, 150.1600341796875,   150.56005859375,\n",
       "          150.9599609375, 151.3599853515625,  151.760009765625,\n",
       "       152.1600341796875,   152.56005859375,    152.9599609375,\n",
       "       153.3599853515625,  153.760009765625, 154.1600341796875,\n",
       "         154.56005859375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xlon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b85fde3a-27c0-4fae-99c3-39b0b3139e9a' class='xr-index-data-in' type='checkbox'/><label for='index-b85fde3a-27c0-4fae-99c3-39b0b3139e9a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-01-02&#x27;, &#x27;2023-01-03&#x27;, &#x27;2023-01-04&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54ac953c-4582-49b0-85dc-0236b8b27df2' class='xr-section-summary-in' type='checkbox'  ><label for='section-54ac953c-4582-49b0-85dc-0236b8b27df2' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>classification_level :</span></dt><dd>UNCLASSIFIED</dd><dt><span>distribution_statement :</span></dt><dd>Approved for public release. Distribution unlimited.</dd><dt><span>downgrade_date :</span></dt><dd>not applicable</dd><dt><span>classification_authority :</span></dt><dd>not applicable</dd><dt><span>institution :</span></dt><dd>Fleet Numerical Meteorology and Oceanography Center</dd><dt><span>source :</span></dt><dd>HYCOM archive file</dd><dt><span>history :</span></dt><dd>archv2ncdf2d</dd><dt><span>comment :</span></dt><dd>p-grid</dd><dt><span>field_type :</span></dt><dd>instantaneous</dd><dt><span>Conventions :</span></dt><dd>CF-1.6 NAVO_netcdf_v1.1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 14kB\n",
       "Dimensions:  (time: 3, ylat: 46, xlon: 25)\n",
       "Coordinates:\n",
       "  * ylat     (ylat) float64 368B -25.0 -24.8 -24.6 -24.4 ... -16.4 -16.2 -16.0\n",
       "  * xlon     (xlon) float64 200B 145.0 145.4 145.8 146.2 ... 153.8 154.2 154.6\n",
       "  * time     (time) datetime64[ns] 24B 2023-01-02 2023-01-03 2023-01-04\n",
       "Data variables:\n",
       "    surf_el  (time, ylat, xlon) float32 14kB ...\n",
       "Attributes:\n",
       "    classification_level:      UNCLASSIFIED\n",
       "    distribution_statement:    Approved for public release. Distribution unli...\n",
       "    downgrade_date:            not applicable\n",
       "    classification_authority:  not applicable\n",
       "    institution:               Fleet Numerical Meteorology and Oceanography C...\n",
       "    source:                    HYCOM archive file\n",
       "    history:                   archv2ncdf2d\n",
       "    comment:                   p-grid\n",
       "    field_type:                instantaneous\n",
       "    Conventions:               CF-1.6 NAVO_netcdf_v1.1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_forcing.elev2D.ds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCHISMDataWave??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "wave_forcing = SCHISMDataWave(\n",
    "        id=\"wavedata\",\n",
    "        source=SourceIntake(\n",
    "            dataset_id=\"ausspec\",\n",
    "            catalog_uri=HERE / \"..\" / \"data\" / \"catalog.yaml\",\n",
    "        ),\n",
    "        coords={'x': \"lon\", 'y': \"lat\"},\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<GeoAxes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x693.878 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = wave_forcing.plot(model_grid=grid)\n",
    "wave_forcing.plot_boundary(ax=ax, grid=grid)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCHISMDataTides?\n",
    "# TidalDataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.schism.data:Generating tides\n",
      "INFO:pyschism.forcing.bctides.bctides:Processing boundary 1:\n",
      "INFO:pyschism.forcing.bctides.bctides:Elevation type: 5\n",
      "WARNING:pyschism.forcing.bctides.bctides:Combination of 3 and 4, time history of elevation is read in from elev2D.th.nc!\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:h_file is rompy/tests/schism/test_data/tpxo9-test/h_m2s2n2.nc\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Velocity type: 3\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:u_file is rompy/tests/schism/test_data/tpxo9-test/u_m2s2n2.nc\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Temperature type: 0\n",
      "WARNING:pyschism.forcing.bctides.bctides:Temperature is not sepcified, not input needed!\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity type: 0\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity is not sepcified, not input needed!\n"
     ]
    }
   ],
   "source": [
    "tidal_forcing = SCHISMDataTides(\n",
    "    tidal_data=TidalDataset(\n",
    "        elevations=HERE / \"test_data\"/ \"tpxo9-test\" / \"h_m2s2n2.nc\",\n",
    "        velocities=HERE / \"test_data\"/ \"tpxo9-test\" / \"u_m2s2n2.nc\" \n",
    "    ),\n",
    "    constituents=[\"M2\", \"S2\", \"N2\"],\n",
    ")\n",
    "tidal_forcing.get(\n",
    "    destdir=workdir,\n",
    "    grid=grid,\n",
    "    time=TimeRange(start=\"2023-01-01\", end=\"2023-01-02\", dt=3600),\n",
    ")\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full config object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 validation error for SchismCSIROConfig\n",
      "  Value error, manning.gr3 must be specified when nchi=-1 [type=value_error, input_value={'grid': SCHISMGrid([145....], sobc=[1], relax=[]))}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.7/v/value_error\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Instantiate a config object\n",
    "\n",
    "from rompy.schism import SchismCSIROConfig\n",
    "from rompy.schism.data import SCHISMData\n",
    "from pydantic import ValidationError\n",
    "try:\n",
    "    config=SchismCSIROConfig(\n",
    "        grid=grid,\n",
    "        data=SCHISMData(\n",
    "            atmos=atmos_forcing, \n",
    "            ocean=ocean_forcing, \n",
    "            wave=wave_forcing, \n",
    "            tides=tidal_forcing\n",
    "            ),\n",
    "    )\n",
    "except ValidationError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rompy.schism.grid:manning is being set to a constant value, this is not recommended. For best results, please supply friction gr3 files with spatially varying values. Further options are under development.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 validation error for SchismCSIROConfig\n",
      "  Value error, manning.gr3 must be specified when nchi=-1 [type=value_error, input_value={'grid': SCHISMGrid([145....], sobc=[1], relax=[]))}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.7/v/value_error\n"
     ]
    }
   ],
   "source": [
    "# That gives us an expected error due to teh fact that we have a validator checking required inputs\n",
    "# Lets fix the grid issue and try again\n",
    "\n",
    "try:\n",
    "    config=SchismCSIROConfig(\n",
    "        grid=grid,\n",
    "        data=SCHISMData(\n",
    "            atmos=atmos_forcing, \n",
    "            ocean=ocean_forcing, \n",
    "            wave=wave_forcing, \n",
    "            tides=tidal_forcing\n",
    "            ),\n",
    "    )\n",
    "except ValidationError as e:\n",
    "    print(e)\n",
    "\n",
    "\n",
    "# Again we get a validation error, the hgrid_WWM is missing. Lets add it and try again\n",
    "\n",
    "grid=SCHISMGrid(\n",
    "    hgrid=DataBlob(id=\"hgrid\", source=hgrid),\n",
    "    manning=1,\n",
    ")\n",
    "config=SchismCSIROConfig(\n",
    "    grid=grid,\n",
    "    mesbf=1,\n",
    "    fricc=0.067,\n",
    "    data=SCHISMData(\n",
    "        atmos=atmos_forcing, \n",
    "        ocean=ocean_forcing, \n",
    "        wave=wave_forcing, \n",
    "        tides=tidal_forcing\n",
    "        ),\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Run\n",
    "\n",
    "Note  that most fields are optional, this eample using defaults values."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Generate workspace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('schism_procedural')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# having trouble using the workdir directory due to filesystem busy issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#workdir = Path('schism_procedural2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic pdb calling has been turned ON\n"
     ]
    }
   ],
   "source": [
    "%pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: test_schism\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-01 12:00:00\n",
      "\tDuration: 12:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: schism_procedural\n",
      "config: <class 'rompy.schism.config.SchismCSIROConfig'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in schism_procedural\n",
      "INFO:rompy.schism.grid:Generated albedo with constant value of 0.15\n",
      "INFO:rompy.schism.grid:Generated diffmin with constant value of 1e-06\n",
      "INFO:rompy.schism.grid:Generated diffmax with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated watertype with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated windrot_geo2proj with constant value of 0.0\n",
      "INFO:rompy.schism.grid:Generated manning with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/test_schism/hgrid.ll\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/test_schism/hgrid_WWM.gr3\n",
      "INFO:rompy.schism.data:Fetching air_1\n",
      "INFO:rompy.schism.data:Fetching elev2D\n",
      "INFO:rompy.schism.data:Fetching wavedata\n",
      "INFO:rompy.schism.data:Generating tides\n",
      "INFO:pyschism.forcing.bctides.bctides:Processing boundary 1:\n",
      "INFO:pyschism.forcing.bctides.bctides:Elevation type: 5\n",
      "WARNING:pyschism.forcing.bctides.bctides:Combination of 3 and 4, time history of elevation is read in from elev2D.th.nc!\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:h_file is rompy/tests/schism/test_data/tpxo9-test/h_m2s2n2.nc\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Velocity type: 3\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:u_file is rompy/tests/schism/test_data/tpxo9-test/u_m2s2n2.nc\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Temperature type: 0\n",
      "WARNING:pyschism.forcing.bctides.bctides:Temperature is not sepcified, not input needed!\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity type: 0\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity is not sepcified, not input needed!\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in schism_procedural\n",
      "INFO:rompy.model:-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#if workdir.exists():\n",
    "#    rmtree(workdir)\n",
    "\n",
    "#workdir.mkdir(exist_ok=True)\n",
    "\n",
    "from rompy.model import ModelRun\n",
    "from rompy.schism import SchismCSIROConfig\n",
    "\n",
    "run = ModelRun(\n",
    "    run_id=\"test_schism\",\n",
    "    period=TimeRange(start=datetime(2023, 1, 1, 0), end=datetime(2023, 1, 1, 12), interval=\"1h\"),\n",
    "    output_dir=str(workdir),\n",
    "    config=config\n",
    ")\n",
    "\n",
    "rundir = run()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets try that again except using a DataBlob and then a DataBlob link for wave forcing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "wave_forcing = DataBlob(id=\"wavedata\", source=\"../../tests/data/wavedata.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rompy.schism.grid:manning is being set to a constant value, this is not recommended. For best results, please supply friction gr3 files with spatially varying values. Further options are under development.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "grid=SCHISMGrid(\n",
    "    hgrid=DataBlob(id=\"hgrid\", source=hgrid),\n",
    "    manning=1,\n",
    ")\n",
    "config=SchismCSIROConfig(\n",
    "    grid=grid,\n",
    "    mesbf=1,\n",
    "    fricc=0.067,\n",
    "    data=SCHISMData(\n",
    "        atmos=atmos_forcing, \n",
    "        ocean=ocean_forcing, \n",
    "        wave=wave_forcing, \n",
    "        tides=tidal_forcing\n",
    "        ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from rompy.model import ModelRun\n",
    "from rompy.schism import SchismCSIROConfig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!rm -rf schism_procedural/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "run = ModelRun(\n",
    "    run_id=\"test_schism\",\n",
    "    period=TimeRange(start=datetime(2023, 1, 1, 0), end=datetime(2023, 1, 1, 12), interval=\"1h\"),\n",
    "    output_dir=str(workdir / '2'),\n",
    "    config=config\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: test_schism\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-01 12:00:00\n",
      "\tDuration: 12:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: schism_procedural/2\n",
      "config: <class 'rompy.schism.config.SchismCSIROConfig'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in schism_procedural/2\n",
      "INFO:rompy.schism.grid:Generated albedo with constant value of 0.15\n",
      "INFO:rompy.schism.grid:Generated diffmin with constant value of 1e-06\n",
      "INFO:rompy.schism.grid:Generated diffmax with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated watertype with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated windrot_geo2proj with constant value of 0.0\n",
      "INFO:rompy.schism.grid:Generated manning with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/2/test_schism/hgrid.ll\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/2/test_schism/hgrid_WWM.gr3\n",
      "INFO:rompy.schism.data:Fetching air_1\n",
      "INFO:rompy.schism.data:Fetching elev2D\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[48], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m rundir \u001b[38;5;241m=\u001b[39m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/rompy/rompy/model.py:141\u001b[0m, in \u001b[0;36mModelRun.__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 141\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/rompy/rompy/model.py:98\u001b[0m, in \u001b[0;36mModelRun.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     93\u001b[0m cc_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mruntime\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_datefmt\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_datefmt})\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig):\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;66;03m# Run the __call__() method of the config object if it is callable passing\u001b[39;00m\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;66;03m# the runtime instance, and fill in the context with what is returned\u001b[39;00m\n\u001b[0;32m---> 98\u001b[0m     cc_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    100\u001b[0m     \u001b[38;5;66;03m# Otherwise just fill in the context with the config instance itself\u001b[39;00m\n\u001b[1;32m    101\u001b[0m     cc_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\n",
      "File \u001b[0;32m~/rompy/rompy/schism/config.py:384\u001b[0m, in \u001b[0;36mSchismCSIROConfig.__call__\u001b[0;34m(self, runtime)\u001b[0m\n\u001b[1;32m    381\u001b[0m ret[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrid\u001b[38;5;241m.\u001b[39mget(runtime\u001b[38;5;241m.\u001b[39mstaging_dir)\n\u001b[1;32m    382\u001b[0m \u001b[38;5;66;03m# TODO Still need to link up these maybe?\u001b[39;00m\n\u001b[1;32m    383\u001b[0m ret\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[0;32m--> 384\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdestdir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mruntime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstaging_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mruntime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperiod\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    387\u001b[0m )\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "File \u001b[0;32m~/rompy/rompy/schism/data.py:694\u001b[0m, in \u001b[0;36mSCHISMData.get\u001b[0;34m(self, destdir, grid, time)\u001b[0m\n\u001b[1;32m    692\u001b[0m     output \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mget(destdir)\n\u001b[1;32m    693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 694\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdestdir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    695\u001b[0m ret\u001b[38;5;241m.\u001b[39mupdate({datatype: output})\n\u001b[1;32m    696\u001b[0m \u001b[38;5;66;03m# ret[\u001b[39;00m\n\u001b[1;32m    697\u001b[0m \u001b[38;5;66;03m#     \"wave\"\u001b[39;00m\n\u001b[1;32m    698\u001b[0m \u001b[38;5;66;03m# ] = \"dummy\"  # Just to make cookiecutter happy if excluding wave forcing\u001b[39;00m\n",
      "File \u001b[0;32m~/rompy/rompy/schism/data.py:547\u001b[0m, in \u001b[0;36mSCHISMDataOcean.get\u001b[0;34m(self, destdir, grid, time)\u001b[0m\n\u001b[1;32m    545\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    546\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 547\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdestdir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/rompy/rompy/schism/data.py:407\u001b[0m, in \u001b[0;36mSCHISMDataBoundary.get\u001b[0;34m(self, destdir, grid, time)\u001b[0m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;66;03m# prepare xarray.Dataset and save forcing netCDF file\u001b[39;00m\n\u001b[1;32m    406\u001b[0m outfile \u001b[38;5;241m=\u001b[39m Path(destdir) \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.th.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 407\u001b[0m boundary_ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mboundary_ds\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    408\u001b[0m boundary_ds\u001b[38;5;241m.\u001b[39mto_netcdf(outfile, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNETCDF3_CLASSIC\u001b[39m\u001b[38;5;124m\"\u001b[39m, unlimited_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outfile\n",
      "File \u001b[0;32m~/rompy/rompy/schism/data.py:414\u001b[0m, in \u001b[0;36mSCHISMDataBoundary.boundary_ds\u001b[0;34m(self, grid, time)\u001b[0m\n\u001b[1;32m    412\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFetching \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    413\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcrop_data \u001b[38;5;129;01mand\u001b[39;00m time \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 414\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_filter_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    415\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sel_boundary(grid)\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ds\u001b[38;5;241m.\u001b[39mtime) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
      "File \u001b[0;32m~/rompy/rompy/core/data.py:413\u001b[0m, in \u001b[0;36mDataGrid._filter_time\u001b[0;34m(self, time, end_buffer)\u001b[0m\n\u001b[1;32m    411\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mend\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoords\u001b[38;5;241m.\u001b[39mt \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mdims:\n\u001b[0;32m--> 413\u001b[0m     dt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mt\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoords\u001b[38;5;241m.\u001b[39mt][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\n\u001b[1;32m    414\u001b[0m     \u001b[38;5;66;03m# Convert to regular timedelta64\u001b[39;00m\n\u001b[1;32m    415\u001b[0m     regular_timedelta \u001b[38;5;241m=\u001b[39m dt\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimedelta64[s]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/dataarray.py:875\u001b[0m, in \u001b[0;36mDataArray.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    872\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_coord(key)\n\u001b[1;32m    873\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    874\u001b[0m     \u001b[38;5;66;03m# xarray-style array indexing\u001b[39;00m\n\u001b[0;32m--> 875\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_item_key_to_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/dataarray.py:1508\u001b[0m, in \u001b[0;36mDataArray.isel\u001b[0;34m(self, indexers, drop, missing_dims, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   1503\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n\u001b[1;32m   1505\u001b[0m \u001b[38;5;66;03m# Much faster algorithm for when all indexers are ints, slices, one-dimensional\u001b[39;00m\n\u001b[1;32m   1506\u001b[0m \u001b[38;5;66;03m# lists, or zero or one-dimensional np.ndarray's\u001b[39;00m\n\u001b[0;32m-> 1508\u001b[0m variable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing_dims\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1509\u001b[0m indexes, index_variables \u001b[38;5;241m=\u001b[39m isel_indexes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mxindexes, indexers)\n\u001b[1;32m   1511\u001b[0m coords \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/variable.py:1033\u001b[0m, in \u001b[0;36mVariable.isel\u001b[0;34m(self, indexers, missing_dims, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   1030\u001b[0m indexers \u001b[38;5;241m=\u001b[39m drop_dims_from_indexers(indexers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims, missing_dims)\n\u001b[1;32m   1032\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(indexers\u001b[38;5;241m.\u001b[39mget(dim, \u001b[38;5;28mslice\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m dim \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims)\n\u001b[0;32m-> 1033\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/variable.py:800\u001b[0m, in \u001b[0;36mVariable.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    797\u001b[0m dims, indexer, new_order \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_broadcast_indexes(key)\n\u001b[1;32m    798\u001b[0m indexable \u001b[38;5;241m=\u001b[39m as_indexable(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data)\n\u001b[0;32m--> 800\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mindexing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_order:\n\u001b[1;32m    803\u001b[0m     data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmoveaxis(data, \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(new_order)), new_order)\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/indexing.py:1026\u001b[0m, in \u001b[0;36mapply_indexer\u001b[0;34m(indexable, indexer)\u001b[0m\n\u001b[1;32m   1024\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m indexable\u001b[38;5;241m.\u001b[39moindex[indexer]\n\u001b[1;32m   1025\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1026\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mindexable\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/indexing.py:1782\u001b[0m, in \u001b[0;36mPandasIndexingAdapter.__getitem__\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m   1779\u001b[0m     indexable \u001b[38;5;241m=\u001b[39m NumpyIndexingAdapter(np\u001b[38;5;241m.\u001b[39masarray(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m   1780\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m indexable[indexer]\n\u001b[0;32m-> 1782\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   1784\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_result(result)\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/pandas/core/indexes/base.py:5389\u001b[0m, in \u001b[0;36mIndex.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   5386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(key) \u001b[38;5;129;01mor\u001b[39;00m is_float(key):\n\u001b[1;32m   5387\u001b[0m     \u001b[38;5;66;03m# GH#44051 exclude bool, which would return a 2d ndarray\u001b[39;00m\n\u001b[1;32m   5388\u001b[0m     key \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mcast_scalar_indexer(key)\n\u001b[0;32m-> 5389\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgetitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5391\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m   5392\u001b[0m     \u001b[38;5;66;03m# This case is separated from the conditional above to avoid\u001b[39;00m\n\u001b[1;32m   5393\u001b[0m     \u001b[38;5;66;03m# pessimization com.is_bool_indexer and ndim checks.\u001b[39;00m\n\u001b[1;32m   5394\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_slice(key)\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:381\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    374\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;124;03mThis getitem defers to the underlying array, which by-definition can\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;124;03monly handle list-likes, slices, and integer scalars\u001b[39;00m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;66;03m# Use cast as we know we will get back a DatetimeLikeArray or DTScalar,\u001b[39;00m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;66;03m# but skip evaluating the Union at runtime for performance\u001b[39;00m\n\u001b[1;32m    380\u001b[0m \u001b[38;5;66;03m# (see https://github.com/pandas-dev/pandas/pull/44624)\u001b[39;00m\n\u001b[0;32m--> 381\u001b[0m result \u001b[38;5;241m=\u001b[39m cast(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnion[Self, DTScalarOrNaT]\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_scalar(result):\n\u001b[1;32m    383\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py:284\u001b[0m, in \u001b[0;36mNDArrayBackedExtensionArray.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\n\u001b[1;32m    279\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    280\u001b[0m     key: PositionalIndexer2D,\n\u001b[1;32m    281\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self \u001b[38;5;241m|\u001b[39m Any:\n\u001b[1;32m    282\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_integer(key):\n\u001b[1;32m    283\u001b[0m         \u001b[38;5;66;03m# fast-path\u001b[39;00m\n\u001b[0;32m--> 284\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ndarray\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    285\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    286\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_box_func(result)\n",
      "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py\u001b[0m(284)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    282 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    283 \u001b[0;31m            \u001b[0;31m# fast-path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m--> 284 \u001b[0;31m            \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ndarray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    285 \u001b[0;31m            \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    286 \u001b[0;31m                \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py\u001b[0m(381)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    379 \u001b[0;31m        \u001b[0;31m# but skip evaluating the Union at runtime for performance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    380 \u001b[0;31m        \u001b[0;31m# (see https://github.com/pandas-dev/pandas/pull/44624)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m--> 381 \u001b[0;31m        \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Union[Self, DTScalarOrNaT]\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    382 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    383 \u001b[0;31m            \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/pandas/core/indexes/base.py\u001b[0m(5389)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m   5387 \u001b[0;31m            \u001b[0;31m# GH#44051 exclude bool, which would return a 2d ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   5388 \u001b[0;31m            \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m-> 5389 \u001b[0;31m            \u001b[0;32mreturn\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   5390 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   5391 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/indexing.py\u001b[0m(1782)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m   1780 \u001b[0;31m            \u001b[0;32mreturn\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1781 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m-> 1782 \u001b[0;31m        \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1783 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1784 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/indexing.py\u001b[0m(1026)\u001b[0;36mapply_indexer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m   1024 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1025 \u001b[0;31m    \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m-> 1026 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1027 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1028 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/variable.py\u001b[0m(800)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    798 \u001b[0;31m        \u001b[0mindexable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    799 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m--> 800 \u001b[0;31m        \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    801 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    802 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0mnew_order\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/variable.py\u001b[0m(1033)\u001b[0;36misel\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m   1031 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1032 \u001b[0;31m        \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m-> 1033 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1034 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1035 \u001b[0;31m    \u001b[0;32mdef\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/dataarray.py\u001b[0m(1508)\u001b[0;36misel\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m   1506 \u001b[0;31m        \u001b[0;31m# lists, or zero or one-dimensional np.ndarray's\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1507 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m-> 1508 \u001b[0;31m        \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmissing_dims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1509 \u001b[0;31m        \u001b[0mindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_variables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misel_indexes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxindexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1510 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/g/data/v95/bl8867/rompyenv/lib/python3.10/site-packages/xarray/core/dataarray.py\u001b[0m(875)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    873 \u001b[0;31m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    874 \u001b[0;31m            \u001b[0;31m# xarray-style array indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m--> 875 \u001b[0;31m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_key_to_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    876 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    877 \u001b[0;31m    \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  u\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/home/599/bl8867/rompy/rompy/core/data.py\u001b[0m(413)\u001b[0;36m_filter_time\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m    411 \u001b[0;31m        \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    412 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m--> 413 \u001b[0;31m            \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    414 \u001b[0;31m            \u001b[0;31m# Convert to regular timedelta64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m    415 \u001b[0;31m            \u001b[0mregular_timedelta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[s]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  len(self.ds[self.coords.t])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "ipdb>  c\n"
     ]
    }
   ],
   "source": [
    "rundir = run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And a DataBlob symlink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "wave_forcing = DataBlob(id=\"wavedata\", source=\"../../tests/data/wavedata.nc\", link=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rompy.schism.grid:manning is being set to a constant value, this is not recommended. For best results, please supply friction gr3 files with spatially varying values. Further options are under development.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "grid=SCHISMGrid(\n",
    "    hgrid=DataBlob(id=\"hgrid\", source=hgrid),\n",
    "    manning=1,\n",
    ")\n",
    "config=SchismCSIROConfig(\n",
    "    grid=grid,\n",
    "    mesbf=1,\n",
    "    fricc=0.067,\n",
    "    data=SCHISMData(\n",
    "        atmos=atmos_forcing, \n",
    "        ocean=ocean_forcing, \n",
    "        wave=wave_forcing, \n",
    "        tides=tidal_forcing\n",
    "        ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('schism_procedural/3')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workdir / '3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: test_schism\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-01 12:00:00\n",
      "\tDuration: 12:00:00\n",
      "\tInterval: 1:00:00\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: schism_procedural/3\n",
      "config: <class 'rompy.schism.config.SchismCSIROConfig'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in schism_procedural/3\n",
      "INFO:rompy.schism.grid:Generated albedo with constant value of 0.15\n",
      "INFO:rompy.schism.grid:Generated diffmin with constant value of 1e-06\n",
      "INFO:rompy.schism.grid:Generated diffmax with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated watertype with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated windrot_geo2proj with constant value of 0.0\n",
      "INFO:rompy.schism.grid:Generated manning with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/3/test_schism/hgrid.ll\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_procedural/3/test_schism/hgrid_WWM.gr3\n",
      "INFO:rompy.schism.data:Fetching air_1\n",
      "INFO:rompy.schism.data:Fetching elev2D\n",
      "INFO:rompy.schism.data:Generating tides\n",
      "INFO:pyschism.forcing.bctides.bctides:Processing boundary 1:\n",
      "INFO:pyschism.forcing.bctides.bctides:Elevation type: 5\n",
      "WARNING:pyschism.forcing.bctides.bctides:Combination of 3 and 4, time history of elevation is read in from elev2D.th.nc!\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:h_file is rompy/tests/schism/test_data/tpxo9-test/h_m2s2n2.nc\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Velocity type: 3\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:u_file is rompy/tests/schism/test_data/tpxo9-test/u_m2s2n2.nc\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Temperature type: 0\n",
      "WARNING:pyschism.forcing.bctides.bctides:Temperature is not sepcified, not input needed!\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity type: 0\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity is not sepcified, not input needed!\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in schism_procedural/3\n",
      "INFO:rompy.model:-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from rompy.model import ModelRun\n",
    "from rompy.schism import SchismCSIROConfig\n",
    "\n",
    "run = ModelRun(\n",
    "    run_id=\"test_schism\",\n",
    "    period=TimeRange(start=datetime(2023, 1, 1, 0), end=datetime(2023, 1, 1, 12), interval=\"1h\"),\n",
    "    output_dir=str(workdir / '3'),\n",
    "    config=config\n",
    ")\n",
    "\n",
    "rundir = run()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run schism\n",
    "(Note that paths to schism binaries will have to be updated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: line 1: mpirun: command not found\n"
     ]
    }
   ],
   "source": [
    "# Run the model \n",
    "!cd schism_procedural/test_schism && mpirun -np 4 /source/schism/src/_VL_WWM 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine outputs\n",
    "!cd schism_procedural/test_schism/outputs && /source/schism/src/Utility/Combining_Scripts/combine_output11.exe  -b 1 -e 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(Path(f\"{rundir}/outputs\").glob(\"*\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load schism files\n",
    "schfile=('schism_procedural/test_schism/outputs/schout_1.nc')\n",
    "schout,meshtri=schism_load(schfile)\n",
    "lons = schout.SCHISM_hgrid_node_y.values\n",
    "lats = schout.SCHISM_hgrid_node_x.values\n",
    "schout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# plot gridded fields - elevation\n",
    "for ix, time in enumerate(schout.time[5:8].values):\n",
    "    \n",
    "    #fig = plt.figure(facecolor='w', figsize=(5,4))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.annotate(pd.to_datetime(time).strftime('%d-%b-%Y %H:00'), fontsize=14, \n",
    "                   xy=(lons.min()+0.0005,lats.max()-0.0002), xycoords='data')\n",
    "    cax=schism_plot(schout, meshtri,'elev', bbox=[145,-25,155,-16], project=True, plotmesh=True, mask=False,\n",
    "              vectors=True, varscale=(-9,9),contours=[0])\n",
    "    ax.tick_params( axis = 'both', which ='major', labelsize = 24)\n",
    "    \n",
    "    fig.subplots_adjust(left=0.05, bottom=0.07, right=0.96, top=0.93)\n",
    "    #figname = '/path_to_your_dir/elev_%02d.png'%ix\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot gridded fields - Hs\n",
    "for ix, time in enumerate(schout.time[5:8].values):\n",
    "    \n",
    "    #fig = plt.figure(facecolor='w', figsize=(5,4))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.annotate(pd.to_datetime(time).strftime('%d-%b-%Y %H:00'), fontsize=14, \n",
    "                   xy=(lons.min()+0.0005,lats.max()-0.0002), xycoords='data')\n",
    "    cax=schism_plot(schout, meshtri,'WWM_1', bbox=[145,-25,155,-16], project=True, plotmesh=True, mask=False,\n",
    "              vectors=True, varscale=(0,5),contours=[0])\n",
    "    ax.tick_params( axis = 'both', which ='major', labelsize = 24)\n",
    "    \n",
    "    fig.subplots_adjust(left=0.05, bottom=0.07, right=0.96, top=0.93)\n",
    "    #figname = '/path_to_your_dir/Hs_%02d.png'%ix\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The full model can be dumped to a configuration file.\n",
    "import yaml\n",
    "# dump full model to yaml\n",
    "with open('model.yaml', 'w') as f:\n",
    "    yaml.dump(run.model_dump(), f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat model.yaml"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running from configuration files. \n",
    "The full model dump above looks complex due to the fact that the full model state, including all default value, is written to the model.yaml file. The same model configuration can be achived in a much simpler file by simply specifying non default values. For example, the entire configuration above is specified in the demo.yaml file shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_dir: schism_declaritive\n",
      "period:\n",
      "  start: 20230101T00\n",
      "  end: 20230101T12\n",
      "  interval: 3600\n",
      "run_id: test_schism\n",
      "config:\n",
      "  model_type: schismcsiro\n",
      "  mesbf: 1\n",
      "  fricc: 0.067\n",
      "  grid:\n",
      "    grid_type: schism\n",
      "    hgrid:\n",
      "      id: hgrid\n",
      "      model_type: data_blob\n",
      "        #source: ../../tests/schism/test_data/hgrid.gr3\n",
      "      source: ../../tests/schism/test_data/hgrid_20kmto60km_rompyschism_testing.gr3\n",
      "    manning: 1\n",
      "  data:\n",
      "    data_type: schism\n",
      "    atmos:\n",
      "      air_1:\n",
      "        source: \n",
      "          uri:  ../../tests/schism/test_data/atmos.nc\n",
      "          model_type: open_dataset\n",
      "        uwind_name: u10\n",
      "        vwind_name: v10\n",
      "        prml_name: mslp\n",
      "        filter:\n",
      "          sort: {coords: [latitude]}\n",
      "        buffer: 5\n",
      "    ocean:\n",
      "      elev2D:\n",
      "        buffer: 0.0\n",
      "        coords:\n",
      "          t: time\n",
      "          x: xlon\n",
      "          y: ylat\n",
      "          z: depth\n",
      "        source: \n",
      "          uri:  ../../tests/schism/test_data/hycom.nc\n",
      "          model_type: open_dataset\n",
      "        variable: surf_el\n",
      "    tides:\n",
      "      constituents:\n",
      "      - M2\n",
      "      - S2\n",
      "      - N2\n",
      "      cutoff_depth: 50.0\n",
      "      flags: \n",
      "        - [5, 3, 0, 0]\n",
      "      tidal_data:\n",
      "        data_type: tidal_dataset\n",
      "        elevations: ../../tests/schism/test_data/tpxo9-neaus/h_m2s2n2.nc\n",
      "        velocities: ../../tests/schism/test_data/tpxo9-neaus/u_m2s2n2.nc\n",
      "    wave:\n",
      "      buffer: 0.0\n",
      "      coords:\n",
      "        t: time\n",
      "        x: lon\n",
      "        y: lat\n",
      "        z: depth\n",
      "      id: wavedata\n",
      "      source:\n",
      "        catalog_uri: ../../tests/data/catalog.yaml\n",
      "        dataset_id: ausspec\n",
      "        model_type: intake\n"
     ]
    }
   ],
   "source": [
    "!cat demo.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rompy.schism.grid:manning is being set to a constant value, this is not recommended. For best results, please supply friction gr3 files with spatially varying values. Further options are under development.\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Model settings:\n",
      "INFO:rompy.model:\n",
      "run_id: test_schism\n",
      "period: \n",
      "\tStart: 2023-01-01 00:00:00\n",
      "\tEnd: 2023-01-01 12:00:00\n",
      "\tDuration: 12:00:00\n",
      "\tInterval: None\n",
      "\tInclude End: True\n",
      "\n",
      "output_dir: schism_declaritive\n",
      "config: <class 'rompy.schism.config.SchismCSIROConfig'>\n",
      "\n",
      "INFO:rompy.model:-----------------------------------------------------\n",
      "INFO:rompy.model:Generating model input files in schism_declaritive\n",
      "INFO:rompy.schism.grid:Generated albedo with constant value of 0.15\n",
      "INFO:rompy.schism.grid:Generated diffmin with constant value of 1e-06\n",
      "INFO:rompy.schism.grid:Generated diffmax with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated watertype with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Generated windrot_geo2proj with constant value of 0.0\n",
      "INFO:rompy.schism.grid:Generated manning with constant value of 1.0\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_declaritive/test_schism/hgrid.ll\n",
      "INFO:rompy.schism.grid:Linking hgrid.gr3 to schism_declaritive/test_schism/hgrid_WWM.gr3\n",
      "INFO:rompy.schism.data:Fetching air_1\n",
      "INFO:rompy.schism.data:Fetching elev2D\n",
      "INFO:rompy.schism.data:Fetching wavedata\n",
      "INFO:rompy.schism.data:Generating tides\n",
      "INFO:pyschism.forcing.bctides.bctides:Processing boundary 1:\n",
      "INFO:pyschism.forcing.bctides.bctides:Elevation type: 5\n",
      "WARNING:pyschism.forcing.bctides.bctides:Combination of 3 and 4, time history of elevation is read in from elev2D.th.nc!\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for elevation constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Velocity type: 3\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent M2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent S2.\n",
      "INFO:pyschism.forcing.bctides.tpxo:Querying TPXO for velocity constituent N2.\n",
      "INFO:pyschism.forcing.bctides.bctides:Temperature type: 0\n",
      "WARNING:pyschism.forcing.bctides.bctides:Temperature is not sepcified, not input needed!\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity type: 0\n",
      "INFO:pyschism.forcing.bctides.bctides:Salinity is not sepcified, not input needed!\n",
      "INFO:rompy.model:\n",
      "INFO:rompy.model:Successfully generated project in schism_declaritive\n",
      "INFO:rompy.model:-----------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/source/rompy/notebooks/schism/schism_declaritive/test_schism'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This can be loaded and used to instatiate the model object and run as above, e.g\n",
    "import yaml\n",
    "demo_config = yaml.load(open('demo.yaml', 'r'), Loader=yaml.FullLoader)\n",
    "run = ModelRun(**demo_config)\n",
    "run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "manning is being set to a constant value, this is not recommended. For best results, please supply friction gr3 files with spatially varying values. Further options are under development.\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/xarray/core/dataset.py:275: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
      "  warnings.warn(\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings:\n",
      "  Expected `Union[BaseConfig, SwanConfig, definition-ref, definition-ref, SchismCSIROConfig]` but got `SchismCSIROConfig` - serialized value may not be as expected\n",
      "  Expected `SfluxSource` but got `str` - serialized value may not be as expected\n",
      "  Expected `dict[any, any]` but got `str` - serialized value may not be as expected\n",
      "  Expected `str` but got `int` - serialized value may not be as expected\n",
      "  return self.__pydantic_serializer__.to_python(\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings:\n",
      "  Expected `SfluxSource` but got `str` - serialized value may not be as expected\n",
      "  Expected `dict[any, any]` but got `str` - serialized value may not be as expected\n",
      "  Expected `str` but got `int` - serialized value may not be as expected\n",
      "  return self.__pydantic_serializer__.to_python(\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/pydantic/main.py:308: UserWarning: Pydantic serializer warnings:\n",
      "  Expected `SfluxSource` but got `str` - serialized value may not be as expected\n",
      "  return self.__pydantic_serializer__.to_python(\n",
      "/source/rompy/rompy/core/filters.py:127: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if k in ds.dims.keys()\n",
      "/source/rompy/rompy/core/filters.py:131: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if (k not in ds.dims.keys()) and (k in ds.coords.keys()):\n",
      "/source/rompy/rompy/core/filters.py:127: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if k in ds.dims.keys()\n",
      "/source/rompy/rompy/core/filters.py:131: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if (k not in ds.dims.keys()) and (k in ds.coords.keys()):\n",
      "/source/rompy/rompy/core/filters.py:127: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if k in ds.dims.keys()\n",
      "/source/rompy/rompy/core/filters.py:131: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if (k not in ds.dims.keys()) and (k in ds.coords.keys()):\n",
      "/source/rompy/rompy/core/filters.py:127: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if k in ds.dims.keys()\n",
      "/source/rompy/rompy/core/filters.py:131: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if (k not in ds.dims.keys()) and (k in ds.coords.keys()):\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/xarray/core/dataset.py:275: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
      "  warnings.warn(\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/xarray/core/dataset.py:275: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
      "  warnings.warn(\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/xarray/core/dataset.py:275: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n",
      "  warnings.warn(\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n",
      "/source/rompy/rompy/core/filters.py:127: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if k in ds.dims.keys()\n",
      "/source/rompy/rompy/core/filters.py:131: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  if (k not in ds.dims.keys()) and (k in ds.coords.keys()):\n",
      "/source/rompy/.venv/lib/python3.11/site-packages/wavespectra/output/ww3.py:108: UserWarning: Times can't be serialized faithfully to int64 with requested units 'days since 1990-01-01'. Resolution of 'hours' needed. Serializing times to floating point instead. Set encoding['dtype'] to integer dtype to serialize to int64. Set encoding['dtype'] to floating point dtype to silence this warning.\n",
      "  other.to_netcdf(filename)\n",
      "Combination of 3 and 4, time history of elevation is read in from elev2D.th.nc!\n",
      "Temperature is not sepcified, not input needed!\n"
     ]
    }
   ],
   "source": [
    "# Alternatively, this same config can be run directly using the rompy cli\n",
    "!rm -fr schism_declaritive #remove previous run\n",
    "!rompy schism demo.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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