A demo of how to use xarrays interpolation to extract a section of velocity.

heuze_cmip6_section_example

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1db47484",
   "metadata": {},
   "source": [
    "# Example how to use pangeo to process CMIP6 models for Céline Heuzé\n",
    "\n",
    "[Inspiration](https://twitter.com/ClnHz/status/1442852277463785482)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c8ae651",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the pangeo CMIP 6 catalog\n",
    "from cmip6_preprocessing.utils import google_cmip_col\n",
    "col = google_cmip_col()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae95e0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets look for a few models with zonal velocity output\n",
    "# this roughly follows the example on the pangeo cmip6 site: https://pangeo-data.github.io/pangeo-cmip6-cloud/accessing_data.html\n",
    "cat = col.search(\n",
    "    variable_id='uo', # use standard CMIP6 vocabulary as input here (https://github.com/WCRP-CMIP/CMIP6_CVs)\n",
    "    experiment_id='historical',\n",
    "    grid_label='gr' # this is a regular lon/lat grid. I will work on an example with native grids later, \n",
    "                    # but the tool needed for that seems broken right now\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15eb381e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 7 dataset(s) from 66 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f509e9",
   "metadata": {},
   "source": [
    "Ok there are 66 total datasets (look at zstore)! Thats too much for this example, lets pick two models for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94878e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     GISS-E2-1-H\n",
       "1     GISS-E2-1-H\n",
       "2     GISS-E2-1-H\n",
       "3     GISS-E2-1-H\n",
       "4     GISS-E2-1-H\n",
       "         ...     \n",
       "61       E3SM-1-0\n",
       "62       E3SM-1-0\n",
       "63     NorESM2-MM\n",
       "64       E3SM-1-0\n",
       "65     NorESM2-MM\n",
       "Name: source_id, Length: 66, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.df['source_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "941f956f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets look for a few models with zonal velocity output\n",
    "cat = col.search(\n",
    "    variable_id='uo', # use standard CMIP6 vocabulary as input here (https://github.com/WCRP-CMIP/CMIP6_CVs)\n",
    "    experiment_id='historical',\n",
    "    grid_label='gr',\n",
    "    source_id = ['E3SM-1-0', 'NorESM2-MM'],\n",
    "    table_id = 'Omon', # only monthly output for now\n",
    "    member_id = 'r1i1p1f1'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9ab379d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
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   "source": [
    "# now lets actually read the zarr stores into xarray datasets \n",
    "#(this happens lazily, e.g. only the metadata is loaded, not all the data)\n",
    "\n",
    "dataset_dict = cat.to_dataset_dict(zarr_kwargs = {'use_cftime':True, 'consolidated':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d72f521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CMIP.NCC.NorESM2-MM.historical.Omon.gr': <xarray.Dataset>\n",
       " Dimensions:             (i: 360, j: 385, lev: 70, bnds: 2, time: 1980, member_id: 1, vertices: 4)\n",
       " Coordinates:\n",
       "   * i                   (i) int32 1 2 3 4 5 6 7 ... 354 355 356 357 358 359 360\n",
       "   * j                   (j) int32 1 2 3 4 5 6 7 ... 379 380 381 382 383 384 385\n",
       "     latitude            (j, i) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "   * lev                 (lev) float64 0.0 5.0 10.0 ... 6.25e+03 6.5e+03 6.75e+03\n",
       "     lev_bnds            (lev, bnds) float64 dask.array<chunksize=(70, 2), meta=np.ndarray>\n",
       "     longitude           (j, i) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "   * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "     time_bnds           (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "   * member_id           (member_id) <U8 'r1i1p1f1'\n",
       " Dimensions without coordinates: bnds, vertices\n",
       " Data variables:\n",
       "     uo                  (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 3, 70, 385, 360), meta=np.ndarray>\n",
       "     vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "     vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       " Attributes: (12/53)\n",
       "     Conventions:               CF-1.7 CMIP-6.2\n",
       "     activity_id:               CMIP\n",
       "     branch_method:             Hybrid-restart from year 1200-01-01 of piControl\n",
       "     branch_time:               0.0\n",
       "     branch_time_in_child:      0.0\n",
       "     branch_time_in_parent:     438000.0\n",
       "     ...                        ...\n",
       "     variable_id:               uo\n",
       "     variant_label:             r1i1p1f1\n",
       "     netcdf_tracking_ids:       hdl:21.14100/63beef79-311b-49a5-ad00-d0c739744...\n",
       "     version_id:                v20191108\n",
       "     intake_esm_varname:        ['uo']\n",
       "     intake_esm_dataset_key:    CMIP.NCC.NorESM2-MM.historical.Omon.gr,\n",
       " 'CMIP.E3SM-Project.E3SM-1-0.historical.Omon.gr': <xarray.Dataset>\n",
       " Dimensions:    (lat: 180, bnds: 2, lev: 60, lon: 360, time: 1980, member_id: 1)\n",
       " Coordinates:\n",
       "   * lat        (lat) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "     lat_bnds   (lat, bnds) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
       "   * lev        (lev) float64 5.0 15.0 25.0 ... 4.875e+03 5.125e+03 5.375e+03\n",
       "     lev_bnds   (lev, bnds) float64 dask.array<chunksize=(60, 2), meta=np.ndarray>\n",
       "   * lon        (lon) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "     lon_bnds   (lon, bnds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>\n",
       "   * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "     time_bnds  (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "   * member_id  (member_id) <U8 'r1i1p1f1'\n",
       " Dimensions without coordinates: bnds\n",
       " Data variables:\n",
       "     uo         (member_id, time, lev, lat, lon) float32 dask.array<chunksize=(1, 11, 60, 180, 360), meta=np.ndarray>\n",
       " Attributes: (12/57)\n",
       "     Conventions:                    CF-1.7 CMIP-6.2\n",
       "     activity_id:                    CMIP\n",
       "     branch_method:                  standard\n",
       "     branch_time_in_child:           0.0\n",
       "     branch_time_in_parent:          36500.0\n",
       "     cmor_version:                   3.4.0\n",
       "     ...                             ...\n",
       "     variant_label:                  r1i1p1f1\n",
       "     status:                         2019-10-25;created;by [email protected]\n",
       "     netcdf_tracking_ids:            hdl:21.14100/864b88e5-f10e-4296-b753-ecaf...\n",
       "     version_id:                     v20190826\n",
       "     intake_esm_varname:             ['uo']\n",
       "     intake_esm_dataset_key:         CMIP.E3SM-Project.E3SM-1-0.historical.Omo...}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a94a220",
   "metadata": {},
   "source": [
    "Did you notice the difference in the naming of the lateral dimensions? (e.g. `i` vs `lon`). This is a common annoyance of the CMIP6 data not being quite 'clean' enough to dig into analysis. [cmip6_preprocessing](https://github.com/jbusecke/cmip6_preprocessing) was written to deal with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f381159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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   "source": [
    "# importantly we use cmip6_preprocessing to clean up the data to have the same dimension naming etc\n",
    "# try it out without the `preprocess\n",
    "\n",
    "from cmip6_preprocessing.preprocessing import combined_preprocessing\n",
    "dataset_dict = cat.to_dataset_dict(zarr_kwargs = {'use_cftime':True, 'consolidated':True}, preprocess=combined_preprocessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f7f4458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CMIP.NCC.NorESM2-MM.historical.Omon.gr': <xarray.Dataset>\n",
       " Dimensions:        (x: 360, y: 385, lev: 70, bnds: 2, time: 1980, member_id: 1, vertex: 4)\n",
       " Coordinates: (12/15)\n",
       "   * x              (x) int32 1 2 3 4 5 6 7 8 ... 353 354 355 356 357 358 359 360\n",
       "   * y              (y) int32 1 2 3 4 5 6 7 8 ... 378 379 380 381 382 383 384 385\n",
       "     lat            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "   * lev            (lev) float64 0.0 5.0 10.0 15.0 ... 6.25e+03 6.5e+03 6.75e+03\n",
       "     lev_bounds     (lev, bnds) float64 dask.array<chunksize=(70, 2), meta=np.ndarray>\n",
       "     lon            (y, x) float64 dask.array<chunksize=(385, 360), meta=np.ndarray>\n",
       "     ...             ...\n",
       "     lon_verticies  (y, x, vertex) float64 dask.array<chunksize=(385, 360, 4), meta=np.ndarray>\n",
       "   * bnds           (bnds) int64 0 1\n",
       "   * vertex         (vertex) int64 0 1 2 3\n",
       "     lon_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       "     lat_bounds     (bnds, y, x) float64 dask.array<chunksize=(1, 385, 360), meta=np.ndarray>\n",
       "   * member_id      (member_id) <U8 'r1i1p1f1'\n",
       " Data variables:\n",
       "     uo             (member_id, time, lev, y, x) float32 dask.array<chunksize=(1, 3, 70, 385, 360), meta=np.ndarray>\n",
       " Attributes: (12/53)\n",
       "     Conventions:               CF-1.7 CMIP-6.2\n",
       "     activity_id:               CMIP\n",
       "     branch_method:             Hybrid-restart from year 1200-01-01 of piControl\n",
       "     branch_time:               0.0\n",
       "     branch_time_in_child:      0.0\n",
       "     branch_time_in_parent:     438000.0\n",
       "     ...                        ...\n",
       "     variable_id:               uo\n",
       "     variant_label:             r1i1p1f1\n",
       "     netcdf_tracking_ids:       hdl:21.14100/63beef79-311b-49a5-ad00-d0c739744...\n",
       "     version_id:                v20191108\n",
       "     intake_esm_varname:        ['uo']\n",
       "     intake_esm_dataset_key:    CMIP.NCC.NorESM2-MM.historical.Omon.gr,\n",
       " 'CMIP.E3SM-Project.E3SM-1-0.historical.Omon.gr': <xarray.Dataset>\n",
       " Dimensions:        (y: 180, bnds: 2, x: 360, lev: 60, time: 1980, member_id: 1, vertex: 4)\n",
       " Coordinates: (12/15)\n",
       "   * y              (y) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "     lat_bounds     (y, bnds, x) float64 dask.array<chunksize=(180, 2, 360), meta=np.ndarray>\n",
       "   * lev            (lev) float64 5.0 15.0 25.0 ... 4.875e+03 5.125e+03 5.375e+03\n",
       "     lev_bounds     (lev, bnds) float64 dask.array<chunksize=(60, 2), meta=np.ndarray>\n",
       "   * x              (x) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "     lon_bounds     (x, bnds, y) float64 dask.array<chunksize=(360, 2, 180), meta=np.ndarray>\n",
       "     ...             ...\n",
       "     lon            (x, y) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5 359.5\n",
       "     lat            (x, y) float64 -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "     lon_verticies  (vertex, x, y) float64 dask.array<chunksize=(1, 360, 180), meta=np.ndarray>\n",
       "     lat_verticies  (vertex, x, y) float64 dask.array<chunksize=(1, 360, 180), meta=np.ndarray>\n",
       "   * vertex         (vertex) int64 0 1 2 3\n",
       "   * member_id      (member_id) <U8 'r1i1p1f1'\n",
       " Data variables:\n",
       "     uo             (member_id, time, lev, y, x) float32 dask.array<chunksize=(1, 11, 60, 180, 360), meta=np.ndarray>\n",
       " Attributes: (12/57)\n",
       "     Conventions:                    CF-1.7 CMIP-6.2\n",
       "     activity_id:                    CMIP\n",
       "     branch_method:                  standard\n",
       "     branch_time_in_child:           0.0\n",
       "     branch_time_in_parent:          36500.0\n",
       "     cmor_version:                   3.4.0\n",
       "     ...                             ...\n",
       "     variant_label:                  r1i1p1f1\n",
       "     status:                         2019-10-25;created;by [email protected]\n",
       "     netcdf_tracking_ids:            hdl:21.14100/864b88e5-f10e-4296-b753-ecaf...\n",
       "     version_id:                     v20190826\n",
       "     intake_esm_varname:             ['uo']\n",
       "     intake_esm_dataset_key:         CMIP.E3SM-Project.E3SM-1-0.historical.Omo...}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b95059d",
   "metadata": {},
   "source": [
    "Tadaa! Now we can get going with the actual analysis!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f12bde9",
   "metadata": {},
   "source": [
    "## Extracting velocity sections\n",
    "\n",
    "Lets start with an example for one model. We can later loop over this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d4b5f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ok lets create the 'section' along which we want to extract data (this will work as indexer for xarray below)\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "n_section = 50\n",
    "section_lon = xr.DataArray(np.linspace(19,21,n_section), dims=\"section\")\n",
    "section_lat = xr.DataArray(np.linspace(68,80,n_section), dims=\"section\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d16b9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_id = 'CMIP.E3SM-Project.E3SM-1-0.historical.Omon.gr'\n",
    "ds = dataset_dict[dataset_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a227897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1cab18e220>]"
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     "execution_count": 34,
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     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets visually confirm that the section is in the right spot\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.NorthPolarStereo()), figsize=[10,10])\n",
    "ds.uo.isel(time=0, lev=0).sel(y=slice(50,None)).plot(transform=ccrs.PlateCarree(), vmax=0.15, ax=ax)\n",
    "ax.plot(section_lon.data, section_lat.data, transform=ccrs.PlateCarree())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790dc584",
   "metadata": {},
   "source": [
    "Might be slightly off, but this can be tuned later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "31d762d5",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;uo&#x27; (member_id: 1, time: 1980, lev: 60, section: 50)&gt;\n",
       "dask.array&lt;dask_aware_interpnd, shape=(1, 1980, 60, 50), dtype=float32, chunksize=(1, 11, 60, 50), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lev        (lev) float64 5.0 15.0 25.0 ... 4.875e+03 5.125e+03 5.375e+03\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    lon        (section) float64 19.0 19.04 19.08 19.12 ... 20.92 20.96 21.0\n",
       "    lat        (section) float64 68.0 68.24 68.49 68.73 ... 79.51 79.76 80.0\n",
       "  * member_id  (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "    x          (section) float64 19.0 19.04 19.08 19.12 ... 20.92 20.96 21.0\n",
       "    y          (section) float64 68.0 68.24 68.49 68.73 ... 79.51 79.76 80.0\n",
       "Dimensions without coordinates: section\n",
       "Attributes:\n",
       "    cell_methods:   time: mean\n",
       "    comment:        Prognostic x-ward velocity component resolved by the model.\n",
       "    long_name:      Sea Water X Velocity\n",
       "    standard_name:  sea_water_x_velocity\n",
       "    units:          m s-1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'uo'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lev</span>: 60</li><li><span>section</span>: 50</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c34db9ad-47fd-4634-a0f2-689fc63a707d' class='xr-array-in' type='checkbox' checked><label for='section-c34db9ad-47fd-4634-a0f2-689fc63a707d' 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=(1, 11, 60, 50), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 22.66 MiB </td>\n",
       "                        <td> 128.91 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 1980, 60, 50) </td>\n",
       "                        <td> (1, 11, 60, 50) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1089 Tasks </td>\n",
       "                        <td> 180 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-8ce56b18-eda3-4608-902d-ba677b018021' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ce56b18-eda3-4608-902d-ba677b018021' class='xr-section-summary' >Coordinates: <span>(7)</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'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 ... 5.125e+03 5.375e+03</div><input id='attrs-65ef15b1-67a6-4163-8e8a-7a5a316dd1b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65ef15b1-67a6-4163-8e8a-7a5a316dd1b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a57366ba-7306-4cc2-9ac6-668192a6fda3' class='xr-var-data-in' type='checkbox'><label for='data-a57366ba-7306-4cc2-9ac6-668192a6fda3' 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>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01,\n",
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       "       1.550000e+02, 1.650984e+02, 1.754790e+02, 1.862912e+02, 1.976603e+02,\n",
       "       2.097113e+02, 2.225783e+02, 2.364088e+02, 2.513701e+02, 2.676542e+02,\n",
       "       2.854836e+02, 3.051192e+02, 3.268679e+02, 3.510934e+02, 3.782275e+02,\n",
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       "       6.388626e+02, 7.075633e+02, 7.870025e+02, 8.788252e+02, 9.847059e+02,\n",
       "       1.106204e+03, 1.244567e+03, 1.400497e+03, 1.573946e+03, 1.764003e+03,\n",
       "       1.968944e+03, 2.186457e+03, 2.413972e+03, 2.649001e+03, 2.889385e+03,\n",
       "       3.133405e+03, 3.379794e+03, 3.627671e+03, 3.876452e+03, 4.125768e+03,\n",
       "       4.375393e+03, 4.625191e+03, 4.875084e+03, 5.125028e+03, 5.375000e+03])</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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-7ba0c0f8-6841-43e8-932d-860f8daa836e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ba0c0f8-6841-43e8-932d-860f8daa836e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8704f83a-7b6c-495c-8768-74880bf10792' class='xr-var-data-in' type='checkbox'><label for='data-8704f83a-7b6c-495c-8768-74880bf10792' 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>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(section)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.0 19.04 19.08 ... 20.96 21.0</div><input id='attrs-d288863b-c47e-46aa-a1fc-ac89a52a68cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d288863b-c47e-46aa-a1fc-ac89a52a68cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86542adf-3976-41d2-bc70-7a42e4fb7edd' class='xr-var-data-in' type='checkbox'><label for='data-86542adf-3976-41d2-bc70-7a42e4fb7edd' 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'></dl></div><div class='xr-var-data'><pre>array([19.        , 19.04081633, 19.08163265, 19.12244898, 19.16326531,\n",
       "       19.20408163, 19.24489796, 19.28571429, 19.32653061, 19.36734694,\n",
       "       19.40816327, 19.44897959, 19.48979592, 19.53061224, 19.57142857,\n",
       "       19.6122449 , 19.65306122, 19.69387755, 19.73469388, 19.7755102 ,\n",
       "       19.81632653, 19.85714286, 19.89795918, 19.93877551, 19.97959184,\n",
       "       20.02040816, 20.06122449, 20.10204082, 20.14285714, 20.18367347,\n",
       "       20.2244898 , 20.26530612, 20.30612245, 20.34693878, 20.3877551 ,\n",
       "       20.42857143, 20.46938776, 20.51020408, 20.55102041, 20.59183673,\n",
       "       20.63265306, 20.67346939, 20.71428571, 20.75510204, 20.79591837,\n",
       "       20.83673469, 20.87755102, 20.91836735, 20.95918367, 21.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(section)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.0 68.24 68.49 ... 79.76 80.0</div><input id='attrs-3ea67a37-6e3d-4854-bc56-6aefcdf257eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ea67a37-6e3d-4854-bc56-6aefcdf257eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-160fa5f3-c9aa-4b6b-8235-8cee473386ed' class='xr-var-data-in' type='checkbox'><label for='data-160fa5f3-c9aa-4b6b-8235-8cee473386ed' 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'></dl></div><div class='xr-var-data'><pre>array([68.        , 68.24489796, 68.48979592, 68.73469388, 68.97959184,\n",
       "       69.2244898 , 69.46938776, 69.71428571, 69.95918367, 70.20408163,\n",
       "       70.44897959, 70.69387755, 70.93877551, 71.18367347, 71.42857143,\n",
       "       71.67346939, 71.91836735, 72.16326531, 72.40816327, 72.65306122,\n",
       "       72.89795918, 73.14285714, 73.3877551 , 73.63265306, 73.87755102,\n",
       "       74.12244898, 74.36734694, 74.6122449 , 74.85714286, 75.10204082,\n",
       "       75.34693878, 75.59183673, 75.83673469, 76.08163265, 76.32653061,\n",
       "       76.57142857, 76.81632653, 77.06122449, 77.30612245, 77.55102041,\n",
       "       77.79591837, 78.04081633, 78.28571429, 78.53061224, 78.7755102 ,\n",
       "       79.02040816, 79.26530612, 79.51020408, 79.75510204, 80.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-99f38fe4-abda-4610-aba2-f75bbac3b6f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99f38fe4-abda-4610-aba2-f75bbac3b6f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d846b21-efba-4161-8f5d-5de38bda0351' class='xr-var-data-in' type='checkbox'><label for='data-8d846b21-efba-4161-8f5d-5de38bda0351' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(section)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.0 19.04 19.08 ... 20.96 21.0</div><input id='attrs-804741bd-3d3d-4630-bbc4-4041b8790f01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-804741bd-3d3d-4630-bbc4-4041b8790f01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc005dd5-c475-4ede-b402-d3b02b8e7180' class='xr-var-data-in' type='checkbox'><label for='data-cc005dd5-c475-4ede-b402-d3b02b8e7180' 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'></dl></div><div class='xr-var-data'><pre>array([19.      , 19.040816, 19.081633, 19.122449, 19.163265, 19.204082,\n",
       "       19.244898, 19.285714, 19.326531, 19.367347, 19.408163, 19.44898 ,\n",
       "       19.489796, 19.530612, 19.571429, 19.612245, 19.653061, 19.693878,\n",
       "       19.734694, 19.77551 , 19.816327, 19.857143, 19.897959, 19.938776,\n",
       "       19.979592, 20.020408, 20.061224, 20.102041, 20.142857, 20.183673,\n",
       "       20.22449 , 20.265306, 20.306122, 20.346939, 20.387755, 20.428571,\n",
       "       20.469388, 20.510204, 20.55102 , 20.591837, 20.632653, 20.673469,\n",
       "       20.714286, 20.755102, 20.795918, 20.836735, 20.877551, 20.918367,\n",
       "       20.959184, 21.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(section)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.0 68.24 68.49 ... 79.76 80.0</div><input id='attrs-e9cb8b52-4cb0-4d09-b947-86cedf9433c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9cb8b52-4cb0-4d09-b947-86cedf9433c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5ab1144-08c5-49e1-bada-15cbf9e7f99d' class='xr-var-data-in' type='checkbox'><label for='data-b5ab1144-08c5-49e1-bada-15cbf9e7f99d' 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'></dl></div><div class='xr-var-data'><pre>array([68.      , 68.244898, 68.489796, 68.734694, 68.979592, 69.22449 ,\n",
       "       69.469388, 69.714286, 69.959184, 70.204082, 70.44898 , 70.693878,\n",
       "       70.938776, 71.183673, 71.428571, 71.673469, 71.918367, 72.163265,\n",
       "       72.408163, 72.653061, 72.897959, 73.142857, 73.387755, 73.632653,\n",
       "       73.877551, 74.122449, 74.367347, 74.612245, 74.857143, 75.102041,\n",
       "       75.346939, 75.591837, 75.836735, 76.081633, 76.326531, 76.571429,\n",
       "       76.816327, 77.061224, 77.306122, 77.55102 , 77.795918, 78.040816,\n",
       "       78.285714, 78.530612, 78.77551 , 79.020408, 79.265306, 79.510204,\n",
       "       79.755102, 80.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d075ac3-90b7-403e-b970-091e37880623' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d075ac3-90b7-403e-b970-091e37880623' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>comment :</span></dt><dd>Prognostic x-ward velocity component resolved by the model.</dd><dt><span>long_name :</span></dt><dd>Sea Water X Velocity</dd><dt><span>standard_name :</span></dt><dd>sea_water_x_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'uo' (member_id: 1, time: 1980, lev: 60, section: 50)>\n",
       "dask.array<dask_aware_interpnd, shape=(1, 1980, 60, 50), dtype=float32, chunksize=(1, 11, 60, 50), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lev        (lev) float64 5.0 15.0 25.0 ... 4.875e+03 5.125e+03 5.375e+03\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    lon        (section) float64 19.0 19.04 19.08 19.12 ... 20.92 20.96 21.0\n",
       "    lat        (section) float64 68.0 68.24 68.49 68.73 ... 79.51 79.76 80.0\n",
       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
       "    x          (section) float64 19.0 19.04 19.08 19.12 ... 20.92 20.96 21.0\n",
       "    y          (section) float64 68.0 68.24 68.49 68.73 ... 79.51 79.76 80.0\n",
       "Dimensions without coordinates: section\n",
       "Attributes:\n",
       "    cell_methods:   time: mean\n",
       "    comment:        Prognostic x-ward velocity component resolved by the model.\n",
       "    long_name:      Sea Water X Velocity\n",
       "    standard_name:  sea_water_x_velocity\n",
       "    units:          m s-1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now for the cool part, interpolate the dataset along this line with xarray\n",
    "u_section = ds.uo.interp(x=section_lon, y=section_lat)\n",
    "u_section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7915c477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1c63395760>"
      ]
     },
     "execution_count": 44,
     "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": [
    "u_section.isel(time=0).sel(lev=slice(0,800)).plot(yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdfeff40",
   "metadata": {},
   "source": [
    "That looks nice! Now lets plot the std of u along that line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e8e7b37b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  2min 46.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/dask/array/numpy_compat.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    }
   ],
   "source": [
    "from dask.diagnostics import ProgressBar\n",
    "with ProgressBar():\n",
    "    u_section_std = u_section.std('time').load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "560dc3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1c604cc250>"
      ]
     },
     "execution_count": 46,
     "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": [
    "u_section_std.sel(lev=slice(0,800)).plot(yincrease=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6f4d5b",
   "metadata": {},
   "source": [
    "We can also save this out to a netcdf (and then download it if you like)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2885eba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  2min 40.4s\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    u_section.to_netcdf('file.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6da26d",
   "metadata": {},
   "source": [
    "You can now loop over all models that are available. This is nice, but we really want to be able to do this analysis with all the models(many of which only have data on the native `gn` grid). This involves another step where we regrid to a regular lon/lat grid. Unfortunatley this functionality is currently [broken](). Ill add this to the example once this is fixed."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}