code/total_ozone_MERRA2_opendap_vs_CMAM30SD.ipynb

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   "id": "a84cb493-3619-42e5-bcdc-74337fcb180f",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
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   "id": "b6bbe3a4-0bae-4fb3-af2b-79a9426976cd",
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   "source": [
    "import xarray as xr\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from netrc import netrc\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from pydap.cas.urs import setup_session\n",
    "import shapely.geometry as sgeom\n",
    "import cartopy.crs as ccrs\n",
    "from dask.distributed import Client, LocalCluster, get_task_stream\n",
    "import sys\n",
    "from PIL import Image\n",
    "from string import ascii_lowercase, ascii_uppercase\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d6fe6c-a1b1-4f36-9735-d1ee8dbe6c51",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1102e434-1114-43d4-a032-90f74875c86b",
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   "outputs": [],
   "source": [
    "def open_date_file(file_path):\n",
    "    df = pd.read_csv(file_path, index_col=0, parse_dates=True)\n",
    "    df['BeginDate'] = df.BeginDate.apply(lambda t: pd.to_datetime(t, format='%Y-%m-%d'))\n",
    "    return df\n",
    "\n",
    "def store_wrappper(dataset_url):\n",
    "    #time.sleep(5)\n",
    "    session = setup_session(username, password, check_url=dataset_url)    \n",
    "    store = xr.backends.PydapDataStore.open(dataset_url, session=session)\n",
    "    return store"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd608c6a-c7b4-4e75-bc0b-6c5522e46d7e",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
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     "data": {
      "text/plain": [
       "<xarray.core.options.set_options at 0x7f8562ff3670>"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.set_options(keep_attrs=True,display_style='html')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25bdb4ef-f8c6-480d-b86b-b52f8631ab56",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "urs = 'urs.earthdata.nasa.gov'\n",
    "netrcDir = os.path.expanduser(\"~/.netrc\")\n",
    "netrc_auth = netrc(netrcDir).authenticators(urs)\n",
    "username = netrc_auth[0]\n",
    "password = netrc_auth[2]\n",
    "data_group = 'M2T1NXCHM.5.12.4' # M2I3NVASM.5.12.4\n",
    "data_type = 'tavg1_2d_chm_Nx'#'inst3_3d_asm_Nv'\n",
    "server_version = 4 # 5\n",
    "basepath = f'https://goldsmr{server_version}.gesdisc.eosdis.nasa.gov/opendap/hyrax/MERRA2/{data_group}'\n",
    "var_ls = 'TO3'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cf1f359-3941-4cf0-aff9-1baad9d7a6f7",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85a8d243-3e6c-4835-9213-eb07b1381cd0",
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    "execution": {
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     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>BeginDate</th>\n",
       "      <th>oGWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1981-02-21</td>\n",
       "      <td>-8.691035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1982-01-21</td>\n",
       "      <td>-6.814002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1982-02-20</td>\n",
       "      <td>-7.509872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1982-12-08</td>\n",
       "      <td>-6.954688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1983-02-14</td>\n",
       "      <td>-7.286437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   BeginDate      oGWD\n",
       "2 1981-02-21 -8.691035\n",
       "3 1982-01-21 -6.814002\n",
       "4 1982-02-20 -7.509872\n",
       "5 1982-12-08 -6.954688\n",
       "6 1983-02-14 -7.286437"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "name = 'himalayas'\n",
    "df_dates = open_date_file(f\"accelogw_{name}_hotspot@70hPa_20dayts_indexes.csv\")\n",
    "df_dates = df_dates[2:] # filter 1979\n",
    "df_dates.head()"
   ]
  },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 43/43 [00:00<00:00, 28380.03it/s]\n"
     ]
    }
   ],
   "source": [
    "lag = 3\n",
    "store_ls = []\n",
    "url_ls = []\n",
    "for dt in tqdm(df_dates.BeginDate, total = df_dates.shape[0]):    # get recent year and month\n",
    "    dt = dt + pd.Timedelta(f'{lag} days') # 3 days after strong OGWD event\n",
    "    year = dt.year\n",
    "    month = dt.month\n",
    "    day = dt.day\n",
    "    \n",
    "    if year >= 1980 and year < 1992:\n",
    "        file_number = '100'\n",
    "    elif year >= 1992 and year < 2001:\n",
    "        file_number = '200'\n",
    "    elif year >= 2001 and year < 2011:\n",
    "        file_number = '300'\n",
    "    elif year >= 2011:\n",
    "        file_number = '400'\n",
    "    elif year == 2000 and  month == 9:\n",
    "        file_number = '401'\n",
    "    else:\n",
    "        raise Exception('The specified year is out of range.')\n",
    "\n",
    "    if month in [12,1,2]:\n",
    "        infile = f'MERRA2_{file_number}.{data_type}.{year}{month:02d}{day:02d}.nc4'\n",
    "        #print(infile)\n",
    "        dataset_url = os.path.join(basepath, \n",
    "                               f'{year}',\n",
    "                               f'{month:02d}',\n",
    "                               infile)\n",
    "        url_ls.append(dataset_url)\n",
    "    #print(dataset_url)\n",
    "    #store = xr.backends.PydapDataStore.open(dataset_url, session=session)\n",
    "    #store_ls.append(store)\n",
    "    #sys.exit()\n",
    "    \n",
    "#ds=xr.open_mfdataset(store_ls)[var_ls]\n",
    "#ds"
   ]
  },
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      "text/plain": [
       "35"
      ]
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     "execution_count": 6,
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   "source": [
    "len(url_ls)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 39,
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    {
     "data": {
      "text/plain": [
       "<xarray.backends.pydap_.PydapDataStore at 0x7f0af1f8db80>"
      ]
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     "execution_count": 39,
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    "results[0]"
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   "cell_type": "code",
   "execution_count": null,
   "id": "d35b3ee9-53b7-4b2a-a75f-be8db9d203c2",
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   "source": [
    "https://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2T1NXCHM.5.12.4/1981/02/MERRA2_100.tavg1_2d_chm_Nx.19810224.nc4.html"
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     "text": [
      "100%|██████████| 35/35 [03:58<00:00,  6.80s/it]\n"
     ]
    }
   ],
   "source": [
    "results = list(tqdm(map(store_wrappper, url_ls), total = len(url_ls)))"
   ]
  },
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
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       "  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",
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       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
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       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
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       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
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       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\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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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
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       "\n",
       ".xr-var-list,\n",
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       ".xr-attrs {\n",
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       "\n",
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       ".xr-var-data {\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "}\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",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TO3&#x27; (time: 840, lat: 361, lon: 576)&gt;\n",
       "dask.array&lt;concatenate, shape=(840, 361, 576), dtype=float32, chunksize=(24, 361, 576), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0\n",
       "  * lon      (lon) float64 -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4\n",
       "  * time     (time) datetime64[ns] 1981-02-24T00:30:00 ... 2010-02-16T23:30:00\n",
       "Attributes:\n",
       "    long_name:       total_column_ozone\n",
       "    units:           Dobsons\n",
       "    fmissing_value:  999999987000000.0\n",
       "    standard_name:   total_column_ozone\n",
       "    vmax:            999999987000000.0\n",
       "    vmin:            -999999987000000.0\n",
       "    valid_range:     [-999999987000000.0, 999999987000000.0]\n",
       "    origname:        TO3\n",
       "    fullnamepath:    /TO3</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'TO3'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 840</li><li><span class='xr-has-index'>lat</span>: 361</li><li><span class='xr-has-index'>lon</span>: 576</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0b8503b1-92b1-4550-8280-dceb38c98da8' class='xr-array-in' type='checkbox' checked><label for='section-0b8503b1-92b1-4550-8280-dceb38c98da8' 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=(24, 361, 576), 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> 666.30 MiB </td> <td> 19.04 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (840, 361, 576) </td> <td> (24, 361, 576) </td></tr>\n",
       "    <tr><th> Count </th><td> 105 Tasks </td><td> 35 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-fc4b9dbe-c6ec-4456-bb49-e6b780259532' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc4b9dbe-c6ec-4456-bb49-e6b780259532' 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'>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 -89.5 -89.0 ... 89.5 90.0</div><input id='attrs-f20decf8-9152-41d1-9506-96cf590b6b06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20decf8-9152-41d1-9506-96cf590b6b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3969c27-333d-450f-904e-a8634618b48c' class='xr-var-data-in' type='checkbox'><label for='data-f3969c27-333d-450f-904e-a8634618b48c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>origname :</span></dt><dd>lat</dd><dt><span>fullnamepath :</span></dt><dd>/lat</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -89.5, -89. , ...,  89. ,  89.5,  90. ])</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'>-180.0 -179.4 ... 178.8 179.4</div><input id='attrs-22765be7-ad2f-4cb5-849d-77f3f3679fa7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22765be7-ad2f-4cb5-849d-77f3f3679fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-954dce0e-66b8-4441-8474-a075a51d8d4d' class='xr-var-data-in' type='checkbox'><label for='data-954dce0e-66b8-4441-8474-a075a51d8d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>origname :</span></dt><dd>lon</dd><dt><span>fullnamepath :</span></dt><dd>/lon</dd></dl></div><div class='xr-var-data'><pre>array([-180.   , -179.375, -178.75 , ...,  178.125,  178.75 ,  179.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-02-24T00:30:00 ... 2010-02-...</div><input id='attrs-cfdce006-51f4-4058-b803-3c8fc365cd0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfdce006-51f4-4058-b803-3c8fc365cd0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5f0d7af-08c4-4404-84fb-68cbdd30eb04' class='xr-var-data-in' type='checkbox'><label for='data-a5f0d7af-08c4-4404-84fb-68cbdd30eb04' 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>time_increment :</span></dt><dd>10000</dd><dt><span>begin_date :</span></dt><dd>19810224</dd><dt><span>begin_time :</span></dt><dd>3000</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>origname :</span></dt><dd>time</dd><dt><span>fullnamepath :</span></dt><dd>/time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-02-24T00:30:00.000000000&#x27;, &#x27;1981-02-24T01:30:00.000000000&#x27;,\n",
       "       &#x27;1981-02-24T02:30:00.000000000&#x27;, ..., &#x27;2010-02-16T21:30:00.000000000&#x27;,\n",
       "       &#x27;2010-02-16T22:30:00.000000000&#x27;, &#x27;2010-02-16T23:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c8b08f5-0fe5-4e70-b562-9523e96d9fcc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c8b08f5-0fe5-4e70-b562-9523e96d9fcc' class='xr-section-summary' >Attributes: <span>(9)</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>total_column_ozone</dd><dt><span>units :</span></dt><dd>Dobsons</dd><dt><span>fmissing_value :</span></dt><dd>999999987000000.0</dd><dt><span>standard_name :</span></dt><dd>total_column_ozone</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-999999987000000.0, 999999987000000.0]</dd><dt><span>origname :</span></dt><dd>TO3</dd><dt><span>fullnamepath :</span></dt><dd>/TO3</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'TO3' (time: 840, lat: 361, lon: 576)>\n",
       "dask.array<concatenate, shape=(840, 361, 576), dtype=float32, chunksize=(24, 361, 576), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0\n",
       "  * lon      (lon) float64 -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4\n",
       "  * time     (time) datetime64[ns] 1981-02-24T00:30:00 ... 2010-02-16T23:30:00\n",
       "Attributes:\n",
       "    long_name:       total_column_ozone\n",
       "    units:           Dobsons\n",
       "    fmissing_value:  999999987000000.0\n",
       "    standard_name:   total_column_ozone\n",
       "    vmax:            999999987000000.0\n",
       "    vmin:            -999999987000000.0\n",
       "    valid_range:     [-999999987000000.0, 999999987000000.0]\n",
       "    origname:        TO3\n",
       "    fullnamepath:    /TO3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds=xr.open_mfdataset(results)[var_ls]\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56d08299-2487-42bd-9ea0-793af7be4860",
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    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'var_ls' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_4016791/962667569.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'/mnt/data02/model/CMAM/0A.daily/{var_ls}_{name}_events_hourly.nc'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#ds.to_netcdf(outfile)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'var_ls' is not defined"
     ]
    }
   ],
   "source": [
    "outfile = f'/mnt/data02/model/CMAM/0A.daily/{var_ls}_{name}_events_hourly.nc'\n",
    "#ds.to_netcdf(outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c345d2-364b-410c-84a8-4f433e755cd3",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf32444-0f5c-4f9e-a6de-77a6586d6b8d",
   "metadata": {},
   "source": [
    "## MERRA2"
   ]
  },
  {
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   "execution_count": 8,
   "id": "403eb7d1-271a-46f5-a9f8-7dc7ecc6e0fa",
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     "shell.execute_reply.started": "2021-11-24T15:05:49.912145Z"
    },
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TO3&#x27; (time: 840, lat: 361, lon: 576)&gt;\n",
       "[174666240 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0\n",
       "  * lon      (lon) float64 -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4\n",
       "  * time     (time) datetime64[ns] 1981-02-24T00:30:00 ... 2010-02-16T23:30:00\n",
       "Attributes:\n",
       "    long_name:       total_column_ozone\n",
       "    units:           Dobsons\n",
       "    fmissing_value:  999999987000000.0\n",
       "    standard_name:   total_column_ozone\n",
       "    vmax:            999999987000000.0\n",
       "    vmin:            -999999987000000.0\n",
       "    valid_range:     [-9.99999987e+14  9.99999987e+14]\n",
       "    origname:        TO3\n",
       "    fullnamepath:    /TO3</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'TO3'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 840</li><li><span class='xr-has-index'>lat</span>: 361</li><li><span class='xr-has-index'>lon</span>: 576</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9b78419d-cbf1-49ed-8679-16dc8d136b1e' class='xr-array-in' type='checkbox' checked><label for='section-9b78419d-cbf1-49ed-8679-16dc8d136b1e' 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>...</span></div><div class='xr-array-data'><pre>[174666240 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-482ffb8d-f12c-4db9-8902-246c3dda98db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-482ffb8d-f12c-4db9-8902-246c3dda98db' 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'>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 -89.5 -89.0 ... 89.5 90.0</div><input id='attrs-2914d618-20ed-4f62-b0ca-92fac6779530' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2914d618-20ed-4f62-b0ca-92fac6779530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba0fe74d-fc23-48bd-b907-3c407cc18ed8' class='xr-var-data-in' type='checkbox'><label for='data-ba0fe74d-fc23-48bd-b907-3c407cc18ed8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-9.99999987e+14  9.99999987e+14]</dd><dt><span>origname :</span></dt><dd>lat</dd><dt><span>fullnamepath :</span></dt><dd>/lat</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -89.5, -89. , ...,  89. ,  89.5,  90. ])</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'>-180.0 -179.4 ... 178.8 179.4</div><input id='attrs-a2e0c568-8aa6-40d6-8e7d-c34de81601ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2e0c568-8aa6-40d6-8e7d-c34de81601ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9edc933a-8679-4836-bfa0-ab538936247d' class='xr-var-data-in' type='checkbox'><label for='data-9edc933a-8679-4836-bfa0-ab538936247d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-9.99999987e+14  9.99999987e+14]</dd><dt><span>origname :</span></dt><dd>lon</dd><dt><span>fullnamepath :</span></dt><dd>/lon</dd></dl></div><div class='xr-var-data'><pre>array([-180.   , -179.375, -178.75 , ...,  178.125,  178.75 ,  179.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-02-24T00:30:00 ... 2010-02-...</div><input id='attrs-e4a23409-dc9e-4b26-9a9b-a5d4b6feac8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4a23409-dc9e-4b26-9a9b-a5d4b6feac8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-327d2ff3-a4a8-4f6b-ba8e-62899703b861' class='xr-var-data-in' type='checkbox'><label for='data-327d2ff3-a4a8-4f6b-ba8e-62899703b861' 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>time_increment :</span></dt><dd>10000</dd><dt><span>begin_date :</span></dt><dd>19810224</dd><dt><span>begin_time :</span></dt><dd>3000</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-9.99999987e+14  9.99999987e+14]</dd><dt><span>origname :</span></dt><dd>time</dd><dt><span>fullnamepath :</span></dt><dd>/time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-02-24T00:30:00.000000000&#x27;, &#x27;1981-02-24T01:30:00.000000000&#x27;,\n",
       "       &#x27;1981-02-24T02:30:00.000000000&#x27;, ..., &#x27;2010-02-16T21:30:00.000000000&#x27;,\n",
       "       &#x27;2010-02-16T22:30:00.000000000&#x27;, &#x27;2010-02-16T23:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c955a54-7306-4ba9-aa59-f017ca9c8fc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c955a54-7306-4ba9-aa59-f017ca9c8fc3' class='xr-section-summary' >Attributes: <span>(9)</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>total_column_ozone</dd><dt><span>units :</span></dt><dd>Dobsons</dd><dt><span>fmissing_value :</span></dt><dd>999999987000000.0</dd><dt><span>standard_name :</span></dt><dd>total_column_ozone</dd><dt><span>vmax :</span></dt><dd>999999987000000.0</dd><dt><span>vmin :</span></dt><dd>-999999987000000.0</dd><dt><span>valid_range :</span></dt><dd>[-9.99999987e+14  9.99999987e+14]</dd><dt><span>origname :</span></dt><dd>TO3</dd><dt><span>fullnamepath :</span></dt><dd>/TO3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'TO3' (time: 840, lat: 361, lon: 576)>\n",
       "[174666240 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.5 -89.0 -88.5 -88.0 ... 88.5 89.0 89.5 90.0\n",
       "  * lon      (lon) float64 -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4\n",
       "  * time     (time) datetime64[ns] 1981-02-24T00:30:00 ... 2010-02-16T23:30:00\n",
       "Attributes:\n",
       "    long_name:       total_column_ozone\n",
       "    units:           Dobsons\n",
       "    fmissing_value:  999999987000000.0\n",
       "    standard_name:   total_column_ozone\n",
       "    vmax:            999999987000000.0\n",
       "    vmin:            -999999987000000.0\n",
       "    valid_range:     [-9.99999987e+14  9.99999987e+14]\n",
       "    origname:        TO3\n",
       "    fullnamepath:    /TO3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outfile = f'/mnt/data04/model/CMAM/0A.daily/{var_ls}_{name}_events_hourly.nc'\n",
    "da = xr.open_dataarray(outfile)\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c890445b-2541-49cc-b13d-3962437930be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:38:59.035183Z",
     "iopub.status.busy": "2021-11-24T15:38:59.034553Z",
     "iopub.status.idle": "2021-11-24T15:39:01.686250Z",
     "shell.execute_reply": "2021-11-24T15:39:01.685498Z",
     "shell.execute_reply.started": "2021-11-24T15:38:59.035115Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "central_longitude = 90\n",
    "projection = ccrs.Orthographic(central_longitude, 90)\n",
    "ax = plt.axes(projection=projection)\n",
    "p = da.mean('time').plot.contourf(transform=ccrs.PlateCarree(), \\\n",
    "           ax = ax, robust = True, extend = 'both',\n",
    "                                  levels = 11) # robust = True, \n",
    "#p.colorbar.set_ticks(ticks)\n",
    "ax = p.axes\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "\n",
    "box = sgeom.box(minx=70, maxx=105, miny=20, maxy=42)\n",
    "ax.add_geometries([box], ccrs.PlateCarree(), \\\n",
    "                  facecolor=['none'], edgecolor=['red'], \\\n",
    "                  linestyle = ['dashed'],\n",
    "                  linewidth = [4])\n",
    "ax.set_title('MERRA2')\n",
    "ax.text(0.05, 0.1, ascii_uppercase[1], transform=ax.transAxes,\n",
    "                  fontsize=24, fontweight='bold', va='top', bbox={'alpha': 0.7, 'facecolor': 'white'})\n",
    "plt.savefig(f'{var_ls}_MERRA2_{name}_composite_verif_fig.pdf', \n",
    "            #dpi = 300, \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
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   "id": "0f6ca62b-d7ff-46b6-9d50-1eb376c9cb6a",
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    },
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   },
   "outputs": [],
   "source": [
    "vmax = p.colorbar.vmax # 430\n",
    "vmin = p.colorbar.vmin # 238\n",
    "ticks = p.colorbar.get_ticks() # [238., 258., 278., 298., 318., 338., 358., 378., 398., 418.]#\n",
    "levels = p.levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "63b9fcd2-d3f1-4227-8b70-acf3e0a4af87",
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     "shell.execute_reply.started": "2021-06-30T13:35:54.161927Z"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([220., 260., 300., 340., 380., 420.])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.colorbar.get_ticks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efca4402-4e6a-4ee5-9d13-a2d1c76f2676",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-11-24T15:07:00.978696Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kuchar/.conda/envs/moje/lib/python3.9/site-packages/distributed/node.py:160: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 33921 instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "<tr>\n",
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       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://195.113.23.121:41189</li>\n",
       "  <li><b>Dashboard: </b><a href='http://195.113.23.121:33921/status' target='_blank'>http://195.113.23.121:33921/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>8</li>\n",
       "  <li><b>Cores: </b>64</li>\n",
       "  <li><b>Memory: </b>503.57 GiB</li>\n",
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    "cluster = LocalCluster(host='195.113.23.121')#, n_workers=4\n",
    "client = Client(cluster) # \n",
    "client"
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   "cell_type": "markdown",
   "id": "0765c840-a441-471f-b8b1-2a9d1fa57334",
   "metadata": {},
   "source": [
    "## CMAM"
   ]
  },
  {
   "cell_type": "code",
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     "shell.execute_reply.started": "2021-11-24T15:07:12.491015Z"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (bnds: 2, lat: 48, lon: 96, time: 46720)\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 00:00:00 ... 2010-12-31 18:00:00\n",
       "  * lat        (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon        (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object dask.array&lt;chunksize=(1460, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (lat, bnds) float64 dask.array&lt;chunksize=(48, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (lon, bnds) float64 dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;\n",
       "    toz        (time, lat, lon) float32 dask.array&lt;chunksize=(1460, 48, 96), meta=np.ndarray&gt;\n",
       "Attributes: (12/30)\n",
       "    institution:            CCCma (Canadian Centre for Climate Modelling and ...\n",
       "    institute_id:           CCCma\n",
       "    experiment_id:          CMAM30-SD\n",
       "    source:                 CMAM 2013\n",
       "    model_id:               CMAM\n",
       "    forcing:                N/A\n",
       "    ...                     ...\n",
       "    table_id:               Table 6hrChem (10 April 2013) 060cf2ef7544c9cc9bc...\n",
       "    title:                  CMAM model output prepared for CMAM30 CMAM 30-yea...\n",
       "    parent_experiment:      N/A\n",
       "    modeling_realm:         atmosChem\n",
       "    realization:            1\n",
       "    cmor_version:           2.8.2</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-251b6cb4-741d-436a-a017-515d77d8c0a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-251b6cb4-741d-436a-a017-515d77d8c0a1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li><li><span class='xr-has-index'>time</span>: 46720</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3e5775bc-3781-4e7c-89af-e71bf62e25f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e5775bc-3781-4e7c-89af-e71bf62e25f4' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 ... 2010-12-...</div><input id='attrs-cb5adcae-451d-4736-ba95-cd14434fc93e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb5adcae-451d-4736-ba95-cd14434fc93e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42ac154d-8b5c-44cc-a987-6f77e2b8ff9e' class='xr-var-data-in' type='checkbox'><label for='data-42ac154d-8b5c-44cc-a987-6f77e2b8ff9e' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</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.datetime(1979, 1, 1, 0, 0, 0, 0, calendar=&#x27;noleap&#x27;, has_year_zero=True),\n",
       "       cftime.datetime(1979, 1, 1, 6, 0, 0, 0, calendar=&#x27;noleap&#x27;, has_year_zero=True),\n",
       "       cftime.datetime(1979, 1, 1, 12, 0, 0, 0, calendar=&#x27;noleap&#x27;, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.datetime(2010, 12, 31, 6, 0, 0, 0, calendar=&#x27;noleap&#x27;, has_year_zero=True),\n",
       "       cftime.datetime(2010, 12, 31, 12, 0, 0, 0, calendar=&#x27;noleap&#x27;, has_year_zero=True),\n",
       "       cftime.datetime(2010, 12, 31, 18, 0, 0, 0, calendar=&#x27;noleap&#x27;, 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-2569bbec-9c9c-4028-8355-5ce4c4093807' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2569bbec-9c9c-4028-8355-5ce4c4093807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d022afbe-fc9d-4302-b5d1-b46cc52bf97c' class='xr-var-data-in' type='checkbox'><label for='data-d022afbe-fc9d-4302-b5d1-b46cc52bf97c' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</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 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-319fd69a-7feb-4827-a59b-037f01829cc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-319fd69a-7feb-4827-a59b-037f01829cc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad73b6e0-f860-450c-a436-2b74222fbb32' class='xr-var-data-in' type='checkbox'><label for='data-ad73b6e0-f860-450c-a436-2b74222fbb32' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab0ea246-b11d-4ead-a137-be9bcd6482da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab0ea246-b11d-4ead-a137-be9bcd6482da' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1460, 2), meta=np.ndarray&gt;</div><input id='attrs-8ccc3ccb-fe34-47eb-9fe1-408f037306f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ccc3ccb-fe34-47eb-9fe1-408f037306f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0a5b7cd-3d11-4148-8ae0-88e9e68545f8' class='xr-var-data-in' type='checkbox'><label for='data-d0a5b7cd-3d11-4148-8ae0-88e9e68545f8' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 2), meta=np.ndarray&gt;</div><input id='attrs-340e74e4-4326-4fc7-9a49-8e6df28f1a97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-340e74e4-4326-4fc7-9a49-8e6df28f1a97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f0d9d1-b809-4297-8cfe-eb47a7fcc772' class='xr-var-data-in' type='checkbox'><label for='data-84f0d9d1-b809-4297-8cfe-eb47a7fcc772' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;</div><input id='attrs-9b3eb2d9-b9b6-4d38-bf55-e6048444b63e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b3eb2d9-b9b6-4d38-bf55-e6048444b63e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2b16311-8115-47eb-b9da-b9455b22abfa' class='xr-var-data-in' type='checkbox'><label for='data-d2b16311-8115-47eb-b9da-b9455b22abfa' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>toz</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1460, 48, 96), meta=np.ndarray&gt;</div><input id='attrs-49a8bf95-d9ad-43dd-809e-d7aac6056436' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49a8bf95-d9ad-43dd-809e-d7aac6056436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d31a1bf8-c175-47f2-a363-1223ed7a8a10' class='xr-var-data-in' type='checkbox'><label for='data-d31a1bf8-c175-47f2-a363-1223ed7a8a10' 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>standard_name :</span></dt><dd>equivalent_thickness_at_stp_of_atmosphere_ozone_content</dd><dt><span>long_name :</span></dt><dd>O3 Column</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T08:50:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20).</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmosChem_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><table>\n",
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       "owner of all intellectual property rights (including copyright) that may exist in this Data \n",
       "product. You (as &quot;The Licensee&quot;) are hereby granted a non-exclusive, non-assignable, \n",
       "non-transferable unrestricted licence to use this data product for any purpose including \n",
       "the right to share these data with others and to make value-added and derivative \n",
       "products from it. This licence is not a sale of any or all of the owner&#x27;s rights.\n",
       "2) NO WARRANTY - This Data product is provided &quot;as-is&quot;; it has not been designed or \n",
       "prepared to meet the Licensee&#x27;s particular requirements. Environment Canada makes no \n",
       "warranty, either express or implied, including but not limited to, warranties of \n",
       "merchantability and fitness for a particular purpose. In no event will Environment Canada \n",
       "be liable for any indirect, special, consequential or other damages attributed to the \n",
       "Licensee&#x27;s use of the Data product.</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>experiment :</span></dt><dd>CMAM 30-year specified dynamics run</dd><dt><span>frequency :</span></dt><dd>6hr</dd><dt><span>creation_date :</span></dt><dd>2013-05-28T08:50:04Z</dd><dt><span>history :</span></dt><dd>2013-05-28T08:50:04Z CMOR rewrote data to comply with CF standards and CMAM30 requirements.</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>project_id :</span></dt><dd>CMAM30</dd><dt><span>table_id :</span></dt><dd>Table 6hrChem (10 April 2013) 060cf2ef7544c9cc9bc3b05656ae17fb</dd><dt><span>title :</span></dt><dd>CMAM model output prepared for CMAM30 CMAM 30-year specified dynamics run</dd><dt><span>parent_experiment :</span></dt><dd>N/A</dd><dt><span>modeling_realm :</span></dt><dd>atmosChem</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>cmor_version :</span></dt><dd>2.8.2</dd></dl></div></li></ul></div></div>"
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       "Attributes: (12/30)\n",
       "    institution:            CCCma (Canadian Centre for Climate Modelling and ...\n",
       "    institute_id:           CCCma\n",
       "    experiment_id:          CMAM30-SD\n",
       "    source:                 CMAM 2013\n",
       "    model_id:               CMAM\n",
       "    forcing:                N/A\n",
       "    ...                     ...\n",
       "    table_id:               Table 6hrChem (10 April 2013) 060cf2ef7544c9cc9bc...\n",
       "    title:                  CMAM model output prepared for CMAM30 CMAM 30-yea...\n",
       "    parent_experiment:      N/A\n",
       "    modeling_realm:         atmosChem\n",
       "    realization:            1\n",
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    "sel_var = 'toz'\n",
    "root_path = '/mnt/data04/model/CMAM/0A.daily/'\n",
    "infiles = f'{root_path}{sel_var}/{sel_var}_6hrChem_CMAM_CMAM30-SD_r1i1p1_*18.nc'\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4016791/1994889851.py:2: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
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       ".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",
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       "\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;toz&#x27; (time: 11680, lat: 48, lon: 96)&gt;\n",
       "array([[[308.28363, 307.89368, 307.53223, ..., 309.63864, 309.15466,\n",
       "         308.70343],\n",
       "        [317.13998, 316.76346, 316.2622 , ..., 317.77594, 317.61285,\n",
       "         317.41296],\n",
       "        [329.89343, 328.66467, 327.1104 , ..., 331.6684 , 331.3939 ,\n",
       "         330.80106],\n",
       "        ...,\n",
       "        [336.3016 , 337.52637, 339.34396, ..., 335.90723, 335.6067 ,\n",
       "         335.6916 ],\n",
       "        [348.33356, 348.4414 , 348.7615 , ..., 349.3319 , 348.79688,\n",
       "         348.45346],\n",
       "        [337.69373, 337.41266, 337.1103 , ..., 338.37726, 338.1805 ,\n",
       "         337.9511 ]],\n",
       "\n",
       "       [[318.87073, 318.77518, 318.65057, ..., 318.9738 , 318.97034,\n",
       "         318.93597],\n",
       "        [318.21732, 317.15076, 316.1012 , ..., 321.28796, 320.31628,\n",
       "         319.2812 ],\n",
       "        [314.86926, 313.74426, 312.87515, ..., 321.1623 , 318.5463 ,\n",
       "         316.43677],\n",
       "...\n",
       "        [309.51028, 308.08466, 306.52853, ..., 313.5514 , 312.21252,\n",
       "         310.87076],\n",
       "        [313.8608 , 311.6558 , 309.636  , ..., 320.79492, 318.49866,\n",
       "         316.17383],\n",
       "        [345.3057 , 345.4164 , 345.6409 , ..., 345.50214, 345.35803,\n",
       "         345.29092]],\n",
       "\n",
       "       [[319.7889 , 319.85443, 319.88922, ..., 319.39667, 319.56073,\n",
       "         319.6913 ],\n",
       "        [317.34277, 317.59042, 317.72427, ..., 316.12494, 316.58954,\n",
       "         317.00183],\n",
       "        [312.3133 , 313.81378, 315.0021 , ..., 307.20288, 308.89377,\n",
       "         310.63086],\n",
       "        ...,\n",
       "        [327.35443, 325.9582 , 324.48166, ..., 329.6124 , 329.3363 ,\n",
       "         328.5351 ],\n",
       "        [326.78955, 326.74426, 326.6013 , ..., 326.37537, 326.59283,\n",
       "         326.73135],\n",
       "        [335.95465, 337.08978, 338.32   , ..., 333.19095, 333.9834 ,\n",
       "         334.91348]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2010-12-31\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "Attributes:\n",
       "    standard_name:     equivalent_thickness_at_stp_of_atmosphere_ozone_content\n",
       "    long_name:         O3 Column\n",
       "    units:             DU\n",
       "    cell_methods:      time:mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2013-05-28T08:50:04Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'toz'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 11680</li><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-05776cfd-6be1-4a7c-bb57-af1386494493' class='xr-array-in' type='checkbox' checked><label for='section-05776cfd-6be1-4a7c-bb57-af1386494493' 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>308.3 307.9 307.5 307.2 306.9 306.6 ... 331.9 332.5 333.2 334.0 334.9</span></div><div class='xr-array-data'><pre>array([[[308.28363, 307.89368, 307.53223, ..., 309.63864, 309.15466,\n",
       "         308.70343],\n",
       "        [317.13998, 316.76346, 316.2622 , ..., 317.77594, 317.61285,\n",
       "         317.41296],\n",
       "        [329.89343, 328.66467, 327.1104 , ..., 331.6684 , 331.3939 ,\n",
       "         330.80106],\n",
       "        ...,\n",
       "        [336.3016 , 337.52637, 339.34396, ..., 335.90723, 335.6067 ,\n",
       "         335.6916 ],\n",
       "        [348.33356, 348.4414 , 348.7615 , ..., 349.3319 , 348.79688,\n",
       "         348.45346],\n",
       "        [337.69373, 337.41266, 337.1103 , ..., 338.37726, 338.1805 ,\n",
       "         337.9511 ]],\n",
       "\n",
       "       [[318.87073, 318.77518, 318.65057, ..., 318.9738 , 318.97034,\n",
       "         318.93597],\n",
       "        [318.21732, 317.15076, 316.1012 , ..., 321.28796, 320.31628,\n",
       "         319.2812 ],\n",
       "        [314.86926, 313.74426, 312.87515, ..., 321.1623 , 318.5463 ,\n",
       "         316.43677],\n",
       "...\n",
       "        [309.51028, 308.08466, 306.52853, ..., 313.5514 , 312.21252,\n",
       "         310.87076],\n",
       "        [313.8608 , 311.6558 , 309.636  , ..., 320.79492, 318.49866,\n",
       "         316.17383],\n",
       "        [345.3057 , 345.4164 , 345.6409 , ..., 345.50214, 345.35803,\n",
       "         345.29092]],\n",
       "\n",
       "       [[319.7889 , 319.85443, 319.88922, ..., 319.39667, 319.56073,\n",
       "         319.6913 ],\n",
       "        [317.34277, 317.59042, 317.72427, ..., 316.12494, 316.58954,\n",
       "         317.00183],\n",
       "        [312.3133 , 313.81378, 315.0021 , ..., 307.20288, 308.89377,\n",
       "         310.63086],\n",
       "        ...,\n",
       "        [327.35443, 325.9582 , 324.48166, ..., 329.6124 , 329.3363 ,\n",
       "         328.5351 ],\n",
       "        [326.78955, 326.74426, 326.6013 , ..., 326.37537, 326.59283,\n",
       "         326.73135],\n",
       "        [335.95465, 337.08978, 338.32   , ..., 333.19095, 333.9834 ,\n",
       "         334.91348]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-d07aec9a-f08a-49f1-ab88-a8522f78aa97' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d07aec9a-f08a-49f1-ab88-a8522f78aa97' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2010-12-31</div><input id='attrs-291c2969-84af-47ee-8dae-8c23de09f185' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-291c2969-84af-47ee-8dae-8c23de09f185' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbc78b30-39ea-4906-8fd9-eddb11fc94a1' class='xr-var-data-in' type='checkbox'><label for='data-bbc78b30-39ea-4906-8fd9-eddb11fc94a1' 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;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-bcde5605-05cb-45ad-8b44-a52871bf340a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcde5605-05cb-45ad-8b44-a52871bf340a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34e16c9f-e93a-4157-bcc6-87f5bb8e39fa' class='xr-var-data-in' type='checkbox'><label for='data-34e16c9f-e93a-4157-bcc6-87f5bb8e39fa' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</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 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-5b015673-d038-4ba9-b758-37e588a97b5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b015673-d038-4ba9-b758-37e588a97b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d0a245d-5792-4106-94f2-2ec2f9ec153c' class='xr-var-data-in' type='checkbox'><label for='data-3d0a245d-5792-4106-94f2-2ec2f9ec153c' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87492249-8665-4b20-a0a6-4ab16a81ba71' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87492249-8665-4b20-a0a6-4ab16a81ba71' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>equivalent_thickness_at_stp_of_atmosphere_ozone_content</dd><dt><span>long_name :</span></dt><dd>O3 Column</dd><dt><span>units :</span></dt><dd>DU</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T08:50:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20).</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmosChem_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'toz' (time: 11680, lat: 48, lon: 96)>\n",
       "array([[[308.28363, 307.89368, 307.53223, ..., 309.63864, 309.15466,\n",
       "         308.70343],\n",
       "        [317.13998, 316.76346, 316.2622 , ..., 317.77594, 317.61285,\n",
       "         317.41296],\n",
       "        [329.89343, 328.66467, 327.1104 , ..., 331.6684 , 331.3939 ,\n",
       "         330.80106],\n",
       "        ...,\n",
       "        [336.3016 , 337.52637, 339.34396, ..., 335.90723, 335.6067 ,\n",
       "         335.6916 ],\n",
       "        [348.33356, 348.4414 , 348.7615 , ..., 349.3319 , 348.79688,\n",
       "         348.45346],\n",
       "        [337.69373, 337.41266, 337.1103 , ..., 338.37726, 338.1805 ,\n",
       "         337.9511 ]],\n",
       "\n",
       "       [[318.87073, 318.77518, 318.65057, ..., 318.9738 , 318.97034,\n",
       "         318.93597],\n",
       "        [318.21732, 317.15076, 316.1012 , ..., 321.28796, 320.31628,\n",
       "         319.2812 ],\n",
       "        [314.86926, 313.74426, 312.87515, ..., 321.1623 , 318.5463 ,\n",
       "         316.43677],\n",
       "...\n",
       "        [309.51028, 308.08466, 306.52853, ..., 313.5514 , 312.21252,\n",
       "         310.87076],\n",
       "        [313.8608 , 311.6558 , 309.636  , ..., 320.79492, 318.49866,\n",
       "         316.17383],\n",
       "        [345.3057 , 345.4164 , 345.6409 , ..., 345.50214, 345.35803,\n",
       "         345.29092]],\n",
       "\n",
       "       [[319.7889 , 319.85443, 319.88922, ..., 319.39667, 319.56073,\n",
       "         319.6913 ],\n",
       "        [317.34277, 317.59042, 317.72427, ..., 316.12494, 316.58954,\n",
       "         317.00183],\n",
       "        [312.3133 , 313.81378, 315.0021 , ..., 307.20288, 308.89377,\n",
       "         310.63086],\n",
       "        ...,\n",
       "        [327.35443, 325.9582 , 324.48166, ..., 329.6124 , 329.3363 ,\n",
       "         328.5351 ],\n",
       "        [326.78955, 326.74426, 326.6013 , ..., 326.37537, 326.59283,\n",
       "         326.73135],\n",
       "        [335.95465, 337.08978, 338.32   , ..., 333.19095, 333.9834 ,\n",
       "         334.91348]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2010-12-31\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "Attributes:\n",
       "    standard_name:     equivalent_thickness_at_stp_of_atmosphere_ozone_content\n",
       "    long_name:         O3 Column\n",
       "    units:             DU\n",
       "    cell_methods:      time:mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2013-05-28T08:50:04Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = ds_cmam['toz'].resample(time = '1D').mean().load()\n",
    "temp['time'] = temp.indexes['time'].to_datetimeindex()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea703539-74dd-4f00-818d-3d8ae9840327",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:08:31.860101Z",
     "iopub.status.busy": "2021-11-24T15:08:31.858351Z",
     "iopub.status.idle": "2021-11-24T15:08:31.875193Z",
     "shell.execute_reply": "2021-11-24T15:08:31.874135Z",
     "shell.execute_reply.started": "2021-11-24T15:08:31.860031Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dates = df_dates.set_index('BeginDate')\n",
    "lag = 3\n",
    "dates = dates.index + pd.Timedelta(str(lag)+' days')\n",
    "#filter lags withi 29th February\n",
    "#dates = dates[dates.apply(lambda x: not (x.day in [29] and x.month in [2]))]\n",
    "dates = dates[~((dates.month == 2) & (dates.day == 29))]\n",
    "#filter dates shited to year out of range\n",
    "#dates = dates[dates.apply(lambda x: not (x.year in [1978,2011]))]\n",
    "dates =  dates[(dates.year != 2011) & (dates.year != 1978)]\n",
    "dates = dates[(dates.month == 12) | (dates.month == 1) | (dates.month == 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ee85732d-2c83-41b8-a4f9-87a8654a8ae6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:39:20.484906Z",
     "iopub.status.busy": "2021-11-24T15:39:20.483890Z",
     "iopub.status.idle": "2021-11-24T15:39:21.288187Z",
     "shell.execute_reply": "2021-11-24T15:39:21.287302Z",
     "shell.execute_reply.started": "2021-11-24T15:39:20.484803Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "central_longitude = 90\n",
    "projection = ccrs.Orthographic(central_longitude, 90)\n",
    "ax = plt.axes(projection=projection)\n",
    "arr = temp.sel(time = dates).mean('time').load()\n",
    "p = arr.plot.contourf(transform=ccrs.PlateCarree(), \\\n",
    "           ax = ax, \n",
    "           vmax = vmax, vmin = vmin, extend = 'both',\n",
    "           levels = levels,\n",
    "           )\n",
    "p.colorbar.set_ticks(ticks)\n",
    "ax = p.axes\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "\n",
    "box = sgeom.box(minx=70, maxx=105, miny=20, maxy=42)\n",
    "ax.add_geometries([box], ccrs.PlateCarree(), \\\n",
    "                  facecolor=['none'], edgecolor=['red'], \\\n",
    "                  linestyle = ['dashed'],\n",
    "                  linewidth = [4])\n",
    "ax.set_title('CMAM30-SD')\n",
    "ax.text(0.05, 0.1, ascii_uppercase[0], transform=ax.transAxes,\n",
    "                  fontsize=24, fontweight='bold', va='top', bbox={'alpha': 0.7, 'facecolor': 'white'})\n",
    "plt.savefig(f'{var_ls}_CMAM30-SD_{name}_composite_verif_fig.pdf', \n",
    "#            dpi = 300, \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daeece2f-e7fc-458d-8b67-cf5cc9f3d021",
   "metadata": {},
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    "## ERA5"
   ]
  },
  {
   "cell_type": "code",
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   "id": "4b0da96a-d9a1-452d-bd3e-1731e70b9e89",
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     "iopub.execute_input": "2022-01-14T10:21:42.636363Z",
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     "iopub.status.idle": "2022-01-14T10:22:00.266224Z",
     "shell.execute_reply": "2022-01-14T10:22:00.265494Z",
     "shell.execute_reply.started": "2022-01-14T10:21:42.636332Z"
    },
    "tags": []
   },
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       "\n",
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       "         483.3294 ],\n",
       "        [483.08447, 483.08447, 483.08447, ..., 483.08447, 483.08447,\n",
       "         483.08447]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ec2d635c-a58b-41b3-bf05-56eb25995c29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec2d635c-a58b-41b3-bf05-56eb25995c29' class='xr-section-summary' >Coordinates: <span>(4)</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>realization</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a81e41c4-130d-4f76-b6c1-8d6aafe980ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a81e41c4-130d-4f76-b6c1-8d6aafe980ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e10609a0-4b34-44d3-9bad-1b6390834a34' class='xr-var-data-in' type='checkbox'><label for='data-e10609a0-4b34-44d3-9bad-1b6390834a34' 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>realization</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>integer</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1981-02-21T01:00:00 ... 2010-02-...</div><input id='attrs-51585624-1991-458e-9fd8-1f8f0b7c9264' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51585624-1991-458e-9fd8-1f8f0b7c9264' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64a4e1b8-e4e8-4a1d-8419-8640f2cd8952' class='xr-var-data-in' type='checkbox'><label for='data-64a4e1b8-e4e8-4a1d-8419-8640f2cd8952' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>stored_direction :</span></dt><dd>increasing</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-02-21T01:00:00.000000000&#x27;, &#x27;1981-02-21T02:00:00.000000000&#x27;,\n",
       "       &#x27;1981-02-21T03:00:00.000000000&#x27;, ..., &#x27;2010-02-13T21:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-13T22:00:00.000000000&#x27;, &#x27;2010-02-13T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><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 -89.75 -89.5 ... 89.75 90.0</div><input id='attrs-f8a6dd12-1876-433a-9a3b-e2849f1e056c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8a6dd12-1876-433a-9a3b-e2849f1e056c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f14aa7b-cf85-4923-b61b-512cb3d4855a' class='xr-var-data-in' type='checkbox'><label for='data-3f14aa7b-cf85-4923-b61b-512cb3d4855a' 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>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</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.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ])</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'>-180.0 -179.8 ... 179.5 179.8</div><input id='attrs-76c5d436-e9e9-48e5-82c2-f4b6d45afb49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76c5d436-e9e9-48e5-82c2-f4b6d45afb49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cfab573-50ab-4ef8-9aa8-e7a572b9f94a' class='xr-var-data-in' type='checkbox'><label for='data-7cfab573-50ab-4ef8-9aa8-e7a572b9f94a' 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>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e22f081c-231d-4cc2-8d66-f114d1953ff6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e22f081c-231d-4cc2-8d66-f114d1953ff6' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>DU</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tco3' (time: 989, lat: 721, lon: 1440)>\n",
       "array([[[246.2416 , 246.2416 , 246.2416 , ..., 246.2416 , 246.2416 ,\n",
       "         246.2416 ],\n",
       "        [247.32152, 247.32152, 247.32152, ..., 247.31595, 247.31595,\n",
       "         247.31595],\n",
       "        [248.76328, 248.76884, 248.76884, ..., 248.7577 , 248.7577 ,\n",
       "         248.76328],\n",
       "        ...,\n",
       "        [402.4912 , 402.48566, 402.48007, ..., 402.50235, 402.49677,\n",
       "         402.49677],\n",
       "        [401.28882, 401.28882, 401.28326, ..., 401.29437, 401.29437,\n",
       "         401.28882],\n",
       "        [400.00848, 400.00848, 400.00848, ..., 400.00848, 400.00848,\n",
       "         400.00848]],\n",
       "\n",
       "       [[244.98318, 244.98318, 244.98318, ..., 244.98318, 244.98318,\n",
       "         244.98318],\n",
       "        [246.04085, 246.04085, 246.04085, ..., 246.03528, 246.03528,\n",
       "         246.03528],\n",
       "        [247.72754, 247.73311, 247.73868, ..., 247.7164 , 247.72197,\n",
       "         247.72754],\n",
       "...\n",
       "        [485.08463, 485.0735 , 485.06235, ..., 485.11246, 485.10132,\n",
       "         485.09018],\n",
       "        [483.92676, 483.9212 , 483.91565, ..., 483.9379 , 483.93234,\n",
       "         483.92676],\n",
       "        [483.48145, 483.48145, 483.48145, ..., 483.48145, 483.48145,\n",
       "         483.48145]],\n",
       "\n",
       "       [[256.84567, 256.84567, 256.84567, ..., 256.84567, 256.84567,\n",
       "         256.84567],\n",
       "        [256.91248, 256.91248, 256.91248, ..., 256.91248, 256.91248,\n",
       "         256.91248],\n",
       "        [256.67868, 256.6731 , 256.6731 , ..., 256.67868, 256.67868,\n",
       "         256.67868],\n",
       "        ...,\n",
       "        [483.79144, 483.78586, 483.7803 , ..., 483.80814, 483.80255,\n",
       "         483.797  ],\n",
       "        [483.32382, 483.32382, 483.31827, ..., 483.33496, 483.3294 ,\n",
       "         483.3294 ],\n",
       "        [483.08447, 483.08447, 483.08447, ..., 483.08447, 483.08447,\n",
       "         483.08447]]], dtype=float32)\n",
       "Coordinates:\n",
       "    realization  int64 ...\n",
       "  * time         (time) datetime64[ns] 1981-02-21T01:00:00 ... 2010-02-13T23:...\n",
       "  * lat          (lat) float64 -90.0 -89.75 -89.5 -89.25 ... 89.5 89.75 90.0\n",
       "  * lon          (lon) float64 -180.0 -179.8 -179.5 -179.2 ... 179.2 179.5 179.8\n",
       "Attributes:\n",
       "    units:    DU"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_ls = 'TO3'\n",
    "name = 'himalayas'\n",
    "\n",
    "era_infile = f'/mnt/data04/model/CMAM/0A.daily/{var_ls}_ERA5_{name}_events_hourly.nc'\n",
    "ds_era = xr.open_dataset(era_infile)\n",
    "to_du = 2.1415e-5 # https://sacs.aeronomie.be/info/dobson.php\n",
    "da_era = ds_era['tco3']/to_du \n",
    "da_era.attrs['units'] = 'DU'\n",
    "da_era"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7dc651df-2e58-46c4-93b4-cb15f25e7d79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T15:39:30.118507Z",
     "iopub.status.busy": "2021-11-24T15:39:30.117309Z",
     "iopub.status.idle": "2021-11-24T15:39:38.433466Z",
     "shell.execute_reply": "2021-11-24T15:39:38.432709Z",
     "shell.execute_reply.started": "2021-11-24T15:39:30.118452Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "central_longitude = 90\n",
    "projection = ccrs.Orthographic(central_longitude, 90)\n",
    "ax = plt.axes(projection=projection)\n",
    "p = da_era.mean('time').plot.contourf(transform=ccrs.PlateCarree(), \\\n",
    "           ax = ax, \n",
    "           vmax = vmax, vmin = vmin, extend = 'both',\n",
    "           levels = levels,\n",
    "           )\n",
    "p.colorbar.set_ticks(ticks)\n",
    "ax = p.axes\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "\n",
    "box = sgeom.box(minx=70, maxx=105, miny=20, maxy=42)\n",
    "ax.add_geometries([box], ccrs.PlateCarree(), \\\n",
    "                  facecolor=['none'], edgecolor=['red'], \\\n",
    "                  linestyle = ['dashed'],\n",
    "                  linewidth = [4])\n",
    "ax.set_title('ERA5')\n",
    "ax.text(0.05, 0.1, ascii_uppercase[2], transform=ax.transAxes,\n",
    "                  fontsize=24, fontweight='bold', va='top', bbox={'alpha': 0.7, 'facecolor': 'white'})\n",
    "plt.savefig(f'{var_ls}_ERA5_{name}_composite_verif_fig.pdf', \n",
    "#            dpi = 300, \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d28cf0-42cd-43df-b457-83a4a5c99094",
   "metadata": {},
   "source": [
    "# MERGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "921ff454-b490-44f6-8e0b-08124d1b05cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-29T09:48:21.113607Z",
     "iopub.status.busy": "2021-07-29T09:48:21.112735Z",
     "iopub.status.idle": "2021-07-29T09:48:21.374254Z",
     "shell.execute_reply": "2021-07-29T09:48:21.373123Z",
     "shell.execute_reply.started": "2021-07-29T09:48:21.113505Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "what_ls = ['CMAM30-SD','MERRA2','ERA5']\n",
    "images = [Image.open(f'{var_ls}_{what}_{name}_composite_verif_fig.png') \n",
    "          for what in what_ls]\n",
    "widths, heights = zip(*(i.size for i in images))\n",
    "\n",
    "total_width = sum(widths)\n",
    "max_height = max(heights)\n",
    "\n",
    "new_im = Image.new('RGB', (total_width, max_height))\n",
    "\n",
    "x_offset = 0\n",
    "for im in images:\n",
    "  new_im.paste(im, (x_offset,0))\n",
    "  x_offset += im.size[0]\n",
    "\n",
    "new_im.save('test.jpg')"
   ]
  }
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