{
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  {
   "cell_type": "markdown",
   "id": "2158d56a118cc6d",
   "metadata": {},
   "source": [
    "# Interpolate hybrid sigma pressure coordinates to pressure\n",
    "\n",
    "Quite a few models usee hybrid sigma coordinates which means that the pressure level follows the surface close to the surface, before becoming less and  influenced by it further up. This is to make it easier numerically to solve the equations. See e.g. here: [link](https://www2.cesm.ucar.edu/models/atm-cam/docs/usersguide/node25.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07e18054f07429a",
   "metadata": {},
   "source": [
    "This means that if we want to e.g. average over a specific pressure level, we have to first interpolate to pressure levels. This is done in the below for an example with CMIP6. It's not a given however that this works for any model, so adjustments might have to be made based on the models vertical coordinates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d61986d-c16f-4b29-9e55-56b7be0b71ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from geocat.comp.interpolation import interp_hybrid_to_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbacfe9b-b5ff-48b9-a430-333e8102dfc5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "xr.set_options(display_style='html')\n",
    "import intake\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from dask.diagnostics import ProgressBar\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d19c48-11e2-4054-9815-ba541ee08370",
   "metadata": {},
   "source": [
    "### Open CMIP6 online catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99657c37-6406-4da0-a77b-e65ce64ad1dd",
   "metadata": {
    "tags": []
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   "outputs": [
    {
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       "<p><strong>pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s)</strong>:</p> <div>\n",
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       "<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>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>170</td>\n",
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       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>657</td>\n",
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       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>37</td>\n",
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       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>514818</td>\n",
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       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>736</td>\n",
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       "    <tr>\n",
       "      <th>derived_variable_id</th>\n",
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   "source": [
    "cat_url = \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(cat_url)\n",
    "col"
   ]
  },
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   "cell_type": "markdown",
   "id": "7d1752b8-a02a-40db-b9c7-8ac5742cea76",
   "metadata": {},
   "source": [
    "\n",
    "### Search corresponding data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02a8cf0-fe87-4233-8beb-a9aea41553c7",
   "metadata": {},
   "source": [
    "Please check [here](https://pangeo-data.github.io/escience-2022/pangeo101/data_discovery.html?highlight=cmip6) for info about CMIP and variables :) \n",
    "\n",
    "Particularly useful is maybe the variable search which you find here: https://clipc-services.ceda.ac.uk/dreq/mipVars.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef05694b-e8b1-4003-909b-93265500cdd6",
   "metadata": {
    "tags": []
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   "outputs": [
    {
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       "\n",
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       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCAR</td>\n",
       "      <td>CESM2</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>AERmon</td>\n",
       "      <td>mmrso4</td>\n",
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       "  activity_id institution_id source_id experiment_id member_id table_id  \\\n",
       "0        CMIP           NCAR     CESM2    historical  r1i1p1f1   AERmon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0      mmrso4         gn  gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...   \n",
       "\n",
       "   dcpp_init_year   version  \n",
       "0             NaN  20190308  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cat = col.search(source_id=['CESM2'], \n",
    "                 experiment_id=['historical'], \n",
    "                 table_id=['AERmon'], \n",
    "                 variable_id=['mmrso4' ], \n",
    "                 member_id=['r1i1p1f1'])\n",
    "cat.df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23256bc7-6009-4bb1-b4c4-08937b00d3a1",
   "metadata": {},
   "source": [
    "### Create dictionary from the list of datasets we found\n",
    "- This step may take several minutes so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0ed2254-03d6-4272-9e59-b5cbdc5fa6b7",
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    "tags": []
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    {
     "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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    "dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "823ec623-83fa-460f-ac5d-ea9cc7c9b4fc",
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    {
     "data": {
      "text/plain": [
       "['CMIP.NCAR.CESM2.historical.AERmon.gn']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
    "list(dset_dict.keys())"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbed9b13-ee76-400a-9869-d82dbac1e897",
   "metadata": {
    "tags": []
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   "source": [
    "ds = dset_dict['CMIP.NCAR.CESM2.historical.AERmon.gn']"
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  },
  {
   "cell_type": "markdown",
   "id": "3401c253-1000-468c-9117-20839ba23de5",
   "metadata": {},
   "source": [
    "### Sub select time period (to make calculations faster):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4fc1a073-4eba-433a-ab64-aef454c2d634",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds = ds.sel(time=slice('2000','2000'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "055122f1-1688-4532-b7e5-92464f6c753e",
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   "outputs": [],
   "source": [
    "hybm= ds['b']\n",
    "hyam = ds['a']\n",
    "ds['lev'] = np.abs(ds['lev'])*100\n",
    "lev_values = ds['lev']\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "d7d72598-95da-4f04-b428-bb26c54aa70a",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "  padding-right: 5px;\n",
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       "\n",
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       "\n",
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       ".xr-var-item > div,\n",
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       ".xr-var-item > .xr-var-name span {\n",
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       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;mmrso4&#x27; (member_id: 1, dcpp_init_year: 1, time: 12, lev: 32,\n",
       "                            lat: 192, lon: 288)&gt; Size: 85MB\n",
       "dask.array&lt;getitem, shape=(1, 1, 12, 32, 192, 288), dtype=float32, chunksize=(1, 1, 10, 32, 192, 288), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
       "  * lev             (lev) float64 256B 364.3 759.5 ... 9.763e+04 9.926e+04\n",
       "  * lon             (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
       "  * time            (time) object 96B 2000-01-15 12:00:00 ... 2000-12-15 12:0...\n",
       "  * member_id       (member_id) object 8B &#x27;r1i1p1f1&#x27;\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
       "Attributes: (12/19)\n",
       "    cell_measures:  area: areacella\n",
       "    cell_methods:   area: time: mean\n",
       "    comment:        Dry mass of sulfate (SO4) in aerosol particles as a fract...\n",
       "    description:    Dry mass of sulfate (SO4) in aerosol particles as a fract...\n",
       "    frequency:      mon\n",
       "    id:             mmrso4\n",
       "    ...             ...\n",
       "    time_label:     time-mean\n",
       "    time_title:     Temporal mean\n",
       "    title:          Aerosol Sulfate Mass Mixing Ratio\n",
       "    type:           real\n",
       "    units:          kg kg-1\n",
       "    variable_id:    mmrso4</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'>'mmrso4'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>dcpp_init_year</span>: 1</li><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>lev</span>: 32</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-35c4d592-50c4-46bc-9696-cd997abcc908' class='xr-array-in' type='checkbox' checked><label for='section-35c4d592-50c4-46bc-9696-cd997abcc908' 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, 1, 10, 32, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <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> 81.00 MiB </td>\n",
       "                        <td> 67.50 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
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       "                        <td> (1, 1, 10, 32, 192, 288) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 4 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-f870ea45-02a7-40a5-a2d1-d0e476497fd4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f870ea45-02a7-40a5-a2d1-d0e476497fd4' class='xr-section-summary' >Coordinates: <span>(6)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-13536fae-706b-4b22-8699-02d17f17dc05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13536fae-706b-4b22-8699-02d17f17dc05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1840fc26-f441-474c-a92f-542f7cf76b5e' class='xr-var-data-in' type='checkbox'><label for='data-1840fc26-f441-474c-a92f-542f7cf76b5e' 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>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
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       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
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       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><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'>364.3 759.5 ... 9.763e+04 9.926e+04</div><input id='attrs-d1bc0c66-7013-4797-b242-9ea02016ead6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1bc0c66-7013-4797-b242-9ea02016ead6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdb89433-cb1c-4288-9d28-02a3393272e2' class='xr-var-data-in' type='checkbox'><label for='data-cdb89433-cb1c-4288-9d28-02a3393272e2' 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([  364.346569,   759.481965,  1435.663225,  2461.222   ,  3592.325002,\n",
       "        4319.375008,  5167.749897,  6152.049825,  7375.095785,  8782.123029,\n",
       "       10331.712663, 12154.724076, 14299.403876, 16822.507977, 19790.80867 ,\n",
       "       23282.861896, 27391.081676, 32224.190235, 37910.090387, 44599.25741 ,\n",
       "       52468.717471, 60977.869481, 69138.943031, 76340.448111, 82085.836865,\n",
       "       85953.476653, 88702.024892, 91264.454694, 93619.839847, 95748.547954,\n",
       "       97632.540739, 99255.609512])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-99c32a0c-ba7d-4d6c-8c82-ca3dc13055d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99c32a0c-ba7d-4d6c-8c82-ca3dc13055d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d75fdcbb-abcc-4acf-93f6-d0c7de27867d' class='xr-var-data-in' type='checkbox'><label for='data-d75fdcbb-abcc-4acf-93f6-d0c7de27867d' 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>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>2000-01-15 12:00:00 ... 2000-12-...</div><input id='attrs-dbbe1d24-f160-48a9-a4b9-89086f537401' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbbe1d24-f160-48a9-a4b9-89086f537401' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41b71a44-dbce-4bc7-b8e9-3736b8b997fe' class='xr-var-data-in' type='checkbox'><label for='data-41b71a44-dbce-4bc7-b8e9-3736b8b997fe' 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>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 12, 15, 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 class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-c28d4a54-0eae-4ea3-855f-02edaf8770c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c28d4a54-0eae-4ea3-855f-02edaf8770c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ea9e9ae-3136-4eaf-9757-4aaadf3976b0' class='xr-var-data-in' type='checkbox'><label for='data-0ea9e9ae-3136-4eaf-9757-4aaadf3976b0' 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=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dcpp_init_year</span></div><div class='xr-var-dims'>(dcpp_init_year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-39b5f6e7-db5e-4e21-8a9a-b1c700aab643' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39b5f6e7-db5e-4e21-8a9a-b1c700aab643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8487de1-ae86-4282-a3ec-86d96b4e199f' class='xr-var-data-in' type='checkbox'><label for='data-c8487de1-ae86-4282-a3ec-86d96b4e199f' 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([nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc8bf649-3644-46b8-80c5-0203c567be62' class='xr-section-summary-in' type='checkbox'  ><label for='section-dc8bf649-3644-46b8-80c5-0203c567be62' class='xr-section-summary' >Indexes: <span>(6)</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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3ad18f3-beb4-4825-bd4a-630078714c3a' class='xr-index-data-in' type='checkbox'/><label for='index-b3ad18f3-beb4-4825-bd4a-630078714c3a' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,   82.4607329842932,  83.40314136125653,\n",
       "        84.34554973821989,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910996,  88.11518324607329,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8de602a-008d-4762-90f5-5ca18ec0075b' class='xr-index-data-in' type='checkbox'/><label for='index-f8de602a-008d-4762-90f5-5ca18ec0075b' 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([  364.346569404006,  759.4819646328688, 1435.6632251292467,\n",
       "       2461.2220004200935, 3592.3250019550323,   4319.37500834465,\n",
       "        5167.749896645546,  6152.049824595451,  7375.095784664154,\n",
       "        8782.123029232025, 10331.712663173676, 12154.724076390266,\n",
       "       14299.403876066208, 16822.507977485657,  19790.80867022276,\n",
       "        23282.86189585924, 27391.081675887108,  32224.19023513794,\n",
       "        37910.09038686752,   44599.2574095726, 52468.717470765114,\n",
       "        60977.86948084831,  69138.94303143024,   76340.4481112957,\n",
       "        82085.83686500788,  85953.47665250301,  88702.02489197254,\n",
       "         91264.4546944648,  93619.83984708786,   95748.5479535535,\n",
       "         97632.5407391414,   99255.6095123291],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5cc5edd6-2925-4fa3-a4df-dd7a84fc4e38' class='xr-index-data-in' type='checkbox'/><label for='index-5cc5edd6-2925-4fa3-a4df-dd7a84fc4e38' 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([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</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-cfea2273-3c2b-412b-8868-36ef65c405bc' class='xr-index-data-in' type='checkbox'/><label for='index-cfea2273-3c2b-412b-8868-36ef65c405bc' 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(CFTimeIndex([2000-01-15 12:00:00, 2000-02-14 00:00:00, 2000-03-15 12:00:00,\n",
       "             2000-04-15 00:00:00, 2000-05-15 12:00:00, 2000-06-15 00:00:00,\n",
       "             2000-07-15 12:00:00, 2000-08-15 12:00:00, 2000-09-15 00:00:00,\n",
       "             2000-10-15 12:00:00, 2000-11-15 00:00:00, 2000-12-15 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=12, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34ce2588-557c-49c5-bdca-f0feb0937acf' class='xr-index-data-in' type='checkbox'/><label for='index-34ce2588-557c-49c5-bdca-f0feb0937acf' 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([&#x27;r1i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dcpp_init_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-96af9538-5b81-421b-b1df-7e9e07698a86' class='xr-index-data-in' type='checkbox'/><label for='index-96af9538-5b81-421b-b1df-7e9e07698a86' 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([nan], dtype=&#x27;float64&#x27;, name=&#x27;dcpp_init_year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6eda59d1-ace4-429d-a652-958af307ef63' class='xr-section-summary-in' type='checkbox'  ><label for='section-6eda59d1-ace4-429d-a652-958af307ef63' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>Dry mass of sulfate (SO4) in aerosol particles as a fraction of air mass.</dd><dt><span>description :</span></dt><dd>Dry mass of sulfate (SO4) in aerosol particles as a fraction of air mass.</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>mmrso4</dd><dt><span>long_name :</span></dt><dd>Aerosol Sulfate Mass Mixing Ratio</dd><dt><span>mipTable :</span></dt><dd>AERmon</dd><dt><span>out_name :</span></dt><dd>mmrso4</dd><dt><span>prov :</span></dt><dd>AERmon ((isd.003))</dd><dt><span>realm :</span></dt><dd>aerosol</dd><dt><span>standard_name :</span></dt><dd>mass_fraction_of_sulfate_dry_aerosol_particles_in_air</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Aerosol Sulfate Mass Mixing Ratio</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>variable_id :</span></dt><dd>mmrso4</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'mmrso4' (member_id: 1, dcpp_init_year: 1, time: 12, lev: 32,\n",
       "                            lat: 192, lon: 288)> Size: 85MB\n",
       "dask.array<getitem, shape=(1, 1, 12, 32, 192, 288), dtype=float32, chunksize=(1, 1, 10, 32, 192, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 2kB -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
       "  * lev             (lev) float64 256B 364.3 759.5 ... 9.763e+04 9.926e+04\n",
       "  * lon             (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
       "  * time            (time) object 96B 2000-01-15 12:00:00 ... 2000-12-15 12:0...\n",
       "  * member_id       (member_id) object 8B 'r1i1p1f1'\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 8B nan\n",
       "Attributes: (12/19)\n",
       "    cell_measures:  area: areacella\n",
       "    cell_methods:   area: time: mean\n",
       "    comment:        Dry mass of sulfate (SO4) in aerosol particles as a fract...\n",
       "    description:    Dry mass of sulfate (SO4) in aerosol particles as a fract...\n",
       "    frequency:      mon\n",
       "    id:             mmrso4\n",
       "    ...             ...\n",
       "    time_label:     time-mean\n",
       "    time_title:     Temporal mean\n",
       "    title:          Aerosol Sulfate Mass Mixing Ratio\n",
       "    type:           real\n",
       "    units:          kg kg-1\n",
       "    variable_id:    mmrso4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = ds['mmrso4']\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99fbea0f-2bb0-4fc6-9807-8bcefab8972f",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
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       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
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       "  padding-bottom: 6px;\n",
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       "\n",
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       "  display: inline-block !important;\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;p0&#x27; ()&gt; Size: 4B\n",
       "array(100000., dtype=float32)\n",
       "Attributes:\n",
       "    long_name:  vertical coordinate formula term: reference pressure\n",
       "    units:      Pa</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'>'p0'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a5bcc628-adfe-464b-a8f4-3e822edf7840' class='xr-array-in' type='checkbox' checked><label for='section-a5bcc628-adfe-464b-a8f4-3e822edf7840' 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>1e+05</span></div><div class='xr-array-data'><pre>array(100000., dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ece62c36-fdd5-4bf5-a744-1cfd13c47187' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ece62c36-fdd5-4bf5-a744-1cfd13c47187' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <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-59145551-0810-4225-9300-0e7a6c93fe99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59145551-0810-4225-9300-0e7a6c93fe99' 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-d04b4531-b194-4b70-91f3-a4b711f47e7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d04b4531-b194-4b70-91f3-a4b711f47e7a' class='xr-section-summary' >Attributes: <span>(2)</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>vertical coordinate formula term: reference pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'p0' ()> Size: 4B\n",
       "array(100000., dtype=float32)\n",
       "Attributes:\n",
       "    long_name:  vertical coordinate formula term: reference pressure\n",
       "    units:      Pa"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['p0'].load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68e5b836-8f9e-46e4-a43e-ab26b2cc19ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a97dc2a-983a-4c4d-910f-363e16ec81b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "da_int = interp_hybrid_to_pressure(ds['mmrso4'],\n",
    "                                   ds['ps'], \n",
    "                                   ds['a'], \n",
    "                                   ds['b'], \n",
    "                                   p0=ds['p0'],\n",
    "                                   new_levels=ds['lev'].values, \n",
    "                                   lev_dim = 'lev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3af85417-b0d5-489f-995b-b04b520c73c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 3.52 ss\n"
     ]
    }
   ],
   "source": [
    "da_mean = ds['mmrso4'].mean('time')\n",
    "with ProgressBar():\n",
    "    da_mean.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2894a63-ff80-43fd-86eb-6c9ec2528b18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[##################################      ] | 85% Completed | 4.66 s ms"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/pangeo-notebook/lib/python3.11/site-packages/geocat/comp/interpolation.py:137: UserWarning: Interpolation point out of data bounds encountered\n",
      "  return func_interpolate(new_levels, xcoords, data, axis=interp_axis)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 5.87 s\n"
     ]
    }
   ],
   "source": [
    "da_int_mean = da_int.mean('time')\n",
    "with ProgressBar():\n",
    "    da_int_mean.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c92edabd-7edd-40e2-a2e3-a4bdf9886d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pressure levels')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2, sharey = True, figsize=[10,4])\n",
    "ax = axs[0]\n",
    "da_mean.mean('lon').plot(ylim=[1000e2,100e2], yscale='log', ax=ax)\n",
    "ax.set_title('Model levels')\n",
    "ax = axs[1]\n",
    "da_int_mean.mean('lon').plot(ylim=[1000e2,100e2], yscale='log', ax=ax)\n",
    "ax.set_title('Pressure levels')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pangeo-notebook]",
   "language": "python",
   "name": "conda-env-pangeo-notebook-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}