jetesdal/MOM6_Overturning_update.ipynb

## MOM6_Overturning_update.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing Meridional Overturning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meriodional Overturning Circulation is a key diagnostic for ocean circulation. This notebook demonstrate how to compute various flavors of it using the [xoverturning](https://github.com/raphaeldussin/xoverturning) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr # requires >= 0.15.1\n",
    "import numpy as np\n",
    "from dask.diagnostics import ProgressBar\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the sample dataset and geolon/geolat from static file (without holes on eliminated processors). Note that this is working for both symetric and non-symetric grids but the dataset and grid file must be consistent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "dataurl = 'http://35.188.34.63:8080/thredds/dodsC/OM4p5/'\n",
    "\n",
    "ds = xr.open_dataset(f'{dataurl}/ocean_monthly_z.200301-200712.nc4',\n",
    "                     chunks={'time':1, 'z_l': 1}, engine='pydap')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = '/work/noaa/gfdlscr/jkrastin/wmt-inert-tracer/OM4p5_sample/'\n",
    "ds = xr.open_dataset(root+'ocean_monthly_z.200301-200712.nc4', chunks={'time':1, 'z_l': 1}, \n",
    "                     drop_variables=['average_DT','average_T1','average_T2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsgrid = xr.open_dataset('ocean_grid_nonsym_OM4_05.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first overturning computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xoverturning import calcmoc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This computes the meridional overturning circulation (moc) on the whole ocean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating basin codes\n"
     ]
    }
   ],
   "source": [
    "moc = calcmoc(ds, dsgrid=dsgrid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is given at the vertical interfaces since the streamfunction is the result of the integration of transports over layers and on the q-point because V-velocities are located on (xh, yq) and integrated over the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  3min  3.6s\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    moc_mean = moc.mean('time').load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remap meridional transport to density coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build an xgcm grid object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgcm import Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure to fill in nans with zeros\n",
    "ds['dxt'] = ds['dxt'].fillna(0.)\n",
    "ds['dyt'] = ds['dyt'].fillna(0.)\n",
    "ds['dzt'] = xr.DataArray(data=ds['z_i'].diff('z_i').values, coords={'z_l': ds['z_l']}, dims=('z_l'))\n",
    "ds['areacello'] = ds['areacello'].fillna(0.)\n",
    "ds['volcello'] = ds['volcello'].fillna(0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = {\n",
    "    ('X',): ['dxt'], # X distances\n",
    "    ('Y',): ['dyt'], # Y distances\n",
    "    ('Z',): ['dzt'],\n",
    "    ('X', 'Y'): ['areacello'], # Areas\n",
    "    ('X', 'Y', 'Z'): ['volcello'], # Volumes\n",
    "}\n",
    "\n",
    "coords={'X': {'center': 'xh', 'right': 'xq'},\n",
    "        'Y': {'center': 'yh', 'right': 'yq'},\n",
    "        'Z': {'center': 'z_l', 'outer': 'z_i'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = Grid(ds, coords=coords, metrics=metrics, periodic=['X'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Density field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gsw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pressure from depth\n",
    "ds['p'] = xr.apply_ufunc(gsw.p_from_z, -ds['z_l'], dsgrid['geolat'], 0, 0, dask='parallelized')\n",
    "\n",
    "# Absolute Salinity (g/kg)\n",
    "ds['sa'] = xr.apply_ufunc(gsw.SA_from_SP, ds['so'], ds['p'], dsgrid['geolon'], dsgrid['geolat'], dask='parallelized')\n",
    "\n",
    "# Conservative temperature (deg C)\n",
    "ds['ct'] = xr.apply_ufunc(gsw.CT_from_t, ds['sa'], ds['thetao'], ds['p'], dask='parallelized')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['sigma1'] = xr.apply_ufunc(gsw.sigma1, ds['sa'], ds['ct'], dask='parallelized')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remap `vmo` with `xgcm.transform`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vmo = ds.vmo.where(ds['wet_v']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define sigma1 density axis\n",
    "sig1_axis = np.concatenate((np.linspace(26,31,51),np.linspace(31.01,33.5,250)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpolate density values to vmo grid\n",
    "sig1 = grid.interp(ds.sigma1, 'Y', boundary='extend').rename('sigma1').chunk({'z_l':-1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform into sigma1 coords\n",
    "vmo_sig1 = grid.transform(vmo.chunk({'z_l':-1}), target = sig1_axis, target_data=sig1, \n",
    "                          method='conservative', mask_edges=True, suffix='_transformed', axis = 'Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate streamfunction\n",
    "psi_sig1 = vmo_sig1.sum('xh').cumsum('sigma1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert kg.s-1 to Sv (1e6 m3.s-1)\n",
    "rho0 = 1035.0\n",
    "moc_sig1 = psi_sig1 / rho0 / 1.0e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 11min 34.6s\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    moc_sig1_mean = moc_sig1.mean('time').load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "moc_mean.plot.contourf(ax=ax, vmin=-30,vmax=30, yincrease=False, levels=np.arange(-30,32,2), cmap='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "moc_sig1_mean.T.plot.contourf(ax=ax, vmin=-30,vmax=30, yincrease=False, levels=np.arange(-30,32,2), cmap='bwr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Density coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because xoverturning is written in a non-specific vertical coordinate system, it can also compute the MOC in other coordinates system, such as the `rho2` output by using the `vertical='rho2'` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pproot = '/archive/Raphael.Dussin/FMS2019.01.03_devgfdl_20201120/'\n",
    "run = 'CM4_piControl_c96_OM4p25_half_kdadd'\n",
    "ppdir = f'{pproot}/{run}/gfdl.ncrc4-intel18-prod-openmp/pp/ocean_annual_rho2'\n",
    "\n",
    "ds_rho2 = xr.open_mfdataset([f\"{ppdir}/ts/annual/10yr/ocean_annual_rho2.0021-0030.umo.nc\",\n",
    "                             f\"{ppdir}/ts/annual/10yr/ocean_annual_rho2.0021-0030.vmo.nc\"])\n",
    "\n",
    "dsgrid_rho2 = xr.open_dataset(f\"{ppdir}/ocean_annual_rho2.static.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating basin codes\n"
     ]
    }
   ],
   "source": [
    "amoc_rho2 = calcmoc(ds_rho2, dsgrid=dsgrid_rho2, vertical='rho2', basin='atl-arc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  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",
       "  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.DataArray &#x27;psi&#x27; (time: 10, rho2_i: 36, yq: 1081)&gt;\n",
       "dask.array&lt;truediv, shape=(10, 36, 1081), dtype=float32, chunksize=(10, 36, 1081), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * rho2_i   (rho2_i) float64 999.5 1.028e+03 1.029e+03 ... 1.037e+03 1.038e+03\n",
       "  * time     (time) object 0021-07-02 12:00:00 ... 0030-07-02 12:00:00\n",
       "  * yq       (yq) float64 -80.43 -80.35 -80.27 -80.19 ... 89.68 89.78 89.89 90.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'psi'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>rho2_i</span>: 36</li><li><span class='xr-has-index'>yq</span>: 1081</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0bb32029-6f84-45de-9d62-c88ebf7897bb' class='xr-array-in' type='checkbox' checked><label for='section-0bb32029-6f84-45de-9d62-c88ebf7897bb' 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>dask.array&lt;chunksize=(10, 36, 1081), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.56 MB </td> <td> 1.56 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (10, 36, 1081) </td> <td> (10, 36, 1081) </td></tr>\n",
       "    <tr><th> Count </th><td> 26 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"194\" height=\"96\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.000000,0.000000 24.948598,14.948598 24.948598,46.967354 10.000000,32.018756\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"10.000000,0.000000 130.000000,0.000000 144.948598,14.948598 24.948598,14.948598\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"24\" y1=\"46\" x2=\"144\" y2=\"46\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"46\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"46\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"24.948598,14.948598 144.948598,14.948598 144.948598,46.967354 24.948598,46.967354\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"84.948598\" y=\"66.967354\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1081</text>\n",
       "  <text x=\"164.948598\" y=\"30.957976\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,30.957976)\">36</text>\n",
       "  <text x=\"7.474299\" y=\"59.493055\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,59.493055)\">10</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-abafdb9b-4d29-489c-a0ec-ec70ac42b385' class='xr-section-summary-in' type='checkbox'  checked><label for='section-abafdb9b-4d29-489c-a0ec-ec70ac42b385' 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'>rho2_i</span></div><div class='xr-var-dims'>(rho2_i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>999.5 1.028e+03 ... 1.038e+03</div><input id='attrs-f32956b4-90ed-4a4a-99f6-fb018baf06de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f32956b4-90ed-4a4a-99f6-fb018baf06de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a219ff1-08d2-41ea-9f24-9f888df8eece' class='xr-var-data-in' type='checkbox'><label for='data-1a219ff1-08d2-41ea-9f24-9f888df8eece' 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>Target Potential Density at interface</dd><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([ 999.5     , 1028.      , 1029.      , 1029.484375, 1029.953125,\n",
       "       1030.40625 , 1030.84375 , 1031.265625, 1031.671875, 1032.0625  ,\n",
       "       1032.4375  , 1032.796875, 1033.140625, 1033.46875 , 1033.78125 ,\n",
       "       1034.078125, 1034.359375, 1034.625   , 1034.875   , 1035.109375,\n",
       "       1035.328125, 1035.53125 , 1035.71875 , 1035.890625, 1036.046875,\n",
       "       1036.1875  , 1036.3125  , 1036.4375  , 1036.5625  , 1036.6875  ,\n",
       "       1036.8125  , 1036.9375  , 1037.0625  , 1037.1875  , 1037.3125  ,\n",
       "       1038.      ])</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'>object</div><div class='xr-var-preview xr-preview'>0021-07-02 12:00:00 ... 0030-07-...</div><input id='attrs-b41a52be-3264-4297-85e1-0eb87d0ddc9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b41a52be-3264-4297-85e1-0eb87d0ddc9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15cb0f4c-545d-43d3-886b-08bd40a7070e' class='xr-var-data-in' type='checkbox'><label for='data-15cb0f4c-545d-43d3-886b-08bd40a7070e' 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>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(21, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(22, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(23, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(24, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(25, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(26, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(27, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(28, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(29, 7, 2, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(30, 7, 2, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yq</span></div><div class='xr-var-dims'>(yq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-80.43 -80.35 -80.27 ... 89.89 90.0</div><input id='attrs-1e1caf00-23c0-45b0-a470-e475d58e30fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e1caf00-23c0-45b0-a470-e475d58e30fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae6f013d-ca71-4e7c-ba34-84d770c4e1f0' class='xr-var-data-in' type='checkbox'><label for='data-ae6f013d-ca71-4e7c-ba34-84d770c4e1f0' 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>q point nominal latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-80.429819, -80.348657, -80.267493, ...,  89.783825,  89.891912,\n",
       "        90.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-703a91c1-fab8-4f1e-8061-1dd693895bb4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-703a91c1-fab8-4f1e-8061-1dd693895bb4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'psi' (time: 10, rho2_i: 36, yq: 1081)>\n",
       "dask.array<truediv, shape=(10, 36, 1081), dtype=float32, chunksize=(10, 36, 1081), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * rho2_i   (rho2_i) float64 999.5 1.028e+03 1.029e+03 ... 1.037e+03 1.038e+03\n",
       "  * time     (time) object 0021-07-02 12:00:00 ... 0030-07-02 12:00:00\n",
       "  * yq       (yq) float64 -80.43 -80.35 -80.27 -80.19 ... 89.68 89.78 89.89 90.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amoc_rho2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2ad484a5d430>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amoc_rho2.sel(time='0022-07').plot(vmin=-30,vmax=30, yincrease=False, cmap='bwr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Derived quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the output of xoverturning is a xarray.DataArray, computing derived quantities is a breeze thanks to xarray built-in functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxmoc_atl = amoc.max(dim=['yq', 'z_i'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ad4868e1ee0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxmoc_atl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxmoc_atl = amoc.sel(yq=slice(20.0, 80.0), z_i=slice(500.0, 2500.0)).max(dim=['yq', 'z_i'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ad484d68340>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxmoc_atl.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misc options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xoverturning also have more specialized options such as `remove_hml` that is used to remove the signal ov `vhml` and a `rotate` option attempting to rotate to true north. This latest option is still in progress and will be updated. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}