notebooks/tests/test-c3s-cmip6-subset-by-point.ipynb

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   "source": [
    "# Run subset by (time) point operation\n",
    "\n",
    "**Rooki** calls climate data operations on the **rook** processing service.\n",
    "\n",
    "\n",
    "See issue: https://github.com/roocs/clisops/issues/230"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "#os.environ['ROOK_URL'] = 'http://rook.dkrz.de/wps'\n",
    "os.environ['ROOK_URL'] = 'http://compute.mips.copernicus-climate.eu/wps'\n",
    "#os.environ['ROOK_URL'] = 'http://rook3.cloud.dkrz.de/wps'\n",
    "\n",
    "from rooki import rooki"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rooki import operators as ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wf = ops.Subset(\n",
    "        ops.Input(\n",
    "            'tas', ['c3s-cmip6.CMIP.NCAR.CESM2-WACCM-FV2.historical.r1i1p1f1.Amon.tas.gn.v20191120']\n",
    "        ),\n",
    "        time=\"1998/2000\",\n",
    "        #time_components=\"year:1998,1999,2000|month:jan,feb,mar,apr,may,jun,jul,aug,sep,oct,nov,dec|day:01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31\"\n",
    "        time_components=\"year:1998,1999,2000|month:jan,feb,mar,apr,may,jun,jul,aug,sep,oct,nov,dec|day:14,15\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp = wf.orchestrate()\n",
    "resp.ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading to /var/folders/qb/mg0csz190wd4rxybhhnwjln80000gn/T/metalink_7cr8bw2x/tas_Amon_CESM2-WACCM-FV2_historical_r1i1p1f1_gn_19980115-20001215.nc.\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 36, lat: 96, lon: 144, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time       (time) object 1998-01-15 12:00:00 ... 2000-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "    time_bnds  (time, nbnd) object 1998-01-01 00:00:00 ... 2001-01-01 00:00:00\n",
       "    lat_bnds   (time, lat, nbnd) float64 -90.95 -89.05 -89.05 ... 89.05 90.95\n",
       "    lon_bnds   (time, lon, nbnd) float64 -1.25 1.25 1.25 ... 356.2 356.2 358.8\n",
       "Attributes: (12/45)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_time_in_parent:  10950.0\n",
       "    case_id:                1562\n",
       "    ...                     ...\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               Amon\n",
       "    tracking_id:            hdl:21.14100/2ebbfd9d-97bf-4858-b893-80d31ffe8cc7\n",
       "    variable_id:            tas\n",
       "    variant_info:           CMIP6 CESM2 historical ensemble with WACCM6-FV2 (...\n",
       "    variant_label:          r1i1p1f1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd86ae67-84f7-47cd-8068-3d8dbeaa5811' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd86ae67-84f7-47cd-8068-3d8dbeaa5811' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 36</li><li><span class='xr-has-index'>lat</span>: 96</li><li><span class='xr-has-index'>lon</span>: 144</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c750f7f-79d1-4170-acbf-0eedd674dba3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c750f7f-79d1-4170-acbf-0eedd674dba3' 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 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-982da8fd-4963-4a57-abc1-6af568f58ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-982da8fd-4963-4a57-abc1-6af568f58ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e30511c8-1a1f-4040-a714-f2d7dd592075' class='xr-var-data-in' type='checkbox'><label for='data-e30511c8-1a1f-4040-a714-f2d7dd592075' 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.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,\n",
       "       -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,\n",
       "       -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,\n",
       "       -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,\n",
       "       -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,\n",
       "        35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,\n",
       "        46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,\n",
       "        69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,\n",
       "        80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  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'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-ac58802c-7bae-4aec-ae47-297eac9c3638' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac58802c-7bae-4aec-ae47-297eac9c3638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3371c003-6aed-4a56-819c-9f62286f7710' class='xr-var-data-in' type='checkbox'><label for='data-3371c003-6aed-4a56-819c-9f62286f7710' 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. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5])</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'>1998-01-15 12:00:00 ... 2000-12-...</div><input id='attrs-ab22a2ab-b07e-4456-9d69-f74c21af578b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab22a2ab-b07e-4456-9d69-f74c21af578b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ab68d2-f5db-4b11-89a0-ba03b7e1ca4b' class='xr-var-data-in' type='checkbox'><label for='data-50ab68d2-f5db-4b11-89a0-ba03b7e1ca4b' 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(1998, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1998, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 3, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 4, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 5, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 6, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 7, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 8, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 9, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1999, 12, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       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></ul></div></li><li class='xr-section-item'><input id='section-2026a11d-a6e8-4540-9b5c-f1ac2f0908d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2026a11d-a6e8-4540-9b5c-f1ac2f0908d5' 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>tas</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'>...</div><input id='attrs-f2c22def-9596-4fb8-b8c1-561ba8ab91fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2c22def-9596-4fb8-b8c1-561ba8ab91fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34abe427-2083-448b-9dcf-fcc754f4ea36' class='xr-var-data-in' type='checkbox'><label for='data-34abe427-2083-448b-9dcf-fcc754f4ea36' 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>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>TREFHT</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>Amon</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>Amon ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</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>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd></dl></div><div class='xr-var-data'><pre>[497664 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a014a57e-160d-4589-9e1a-526aedab80f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a014a57e-160d-4589-9e1a-526aedab80f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16b8a35e-0a2a-4796-b7a2-c34632f3c44c' class='xr-var-data-in' type='checkbox'><label for='data-16b8a35e-0a2a-4796-b7a2-c34632f3c44c' 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([[cftime.DatetimeNoLeap(1998, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 5, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 6, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 7, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 8, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 9, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 10, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1998, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1998, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 5, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 6, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 7, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 8, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 9, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 10, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1999, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1999, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 5, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 5, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 6, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 6, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 7, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 7, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 8, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 8, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 9, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 9, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 10, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2001, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f2f66db0-47ed-4473-b7dc-08615083682d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2f66db0-47ed-4473-b7dc-08615083682d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9bda57-12a4-4951-9944-f9db178475a2' class='xr-var-data-in' type='checkbox'><label for='data-fd9bda57-12a4-4951-9944-f9db178475a2' 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([[[-90.947368, -89.052632],\n",
       "        [-89.052632, -87.157895],\n",
       "        ...,\n",
       "        [ 87.157895,  89.052632],\n",
       "        [ 89.052632,  90.947368]],\n",
       "\n",
       "       [[-90.947368, -89.052632],\n",
       "        [-89.052632, -87.157895],\n",
       "        ...,\n",
       "        [ 87.157895,  89.052632],\n",
       "        [ 89.052632,  90.947368]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-90.947368, -89.052632],\n",
       "        [-89.052632, -87.157895],\n",
       "        ...,\n",
       "        [ 87.157895,  89.052632],\n",
       "        [ 89.052632,  90.947368]],\n",
       "\n",
       "       [[-90.947368, -89.052632],\n",
       "        [-89.052632, -87.157895],\n",
       "        ...,\n",
       "        [ 87.157895,  89.052632],\n",
       "        [ 89.052632,  90.947368]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-78ce6ea8-b395-4a6a-91c6-6f258956d974' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78ce6ea8-b395-4a6a-91c6-6f258956d974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f94224-4e30-4100-8b00-1b093210b4ed' class='xr-var-data-in' type='checkbox'><label for='data-37f94224-4e30-4100-8b00-1b093210b4ed' 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([[[ -1.25,   1.25],\n",
       "        [  1.25,   3.75],\n",
       "        ...,\n",
       "        [353.75, 356.25],\n",
       "        [356.25, 358.75]],\n",
       "\n",
       "       [[ -1.25,   1.25],\n",
       "        [  1.25,   3.75],\n",
       "        ...,\n",
       "        [353.75, 356.25],\n",
       "        [356.25, 358.75]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ -1.25,   1.25],\n",
       "        [  1.25,   3.75],\n",
       "        ...,\n",
       "        [353.75, 356.25],\n",
       "        [356.25, 358.75]],\n",
       "\n",
       "       [[ -1.25,   1.25],\n",
       "        [  1.25,   3.75],\n",
       "        ...,\n",
       "        [353.75, 356.25],\n",
       "        [356.25, 358.75]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3254175-f9df-4ee1-a528-6b97bd40d341' class='xr-section-summary-in' type='checkbox'  ><label for='section-e3254175-f9df-4ee1-a528-6b97bd40d341' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>10950.0</dd><dt><span>case_id :</span></dt><dd>1562</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BWmaHIST.f19_g17.CMIP6-historical-WACCM-MA-2deg.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-08-31T19:49:09Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation. </dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2-WACCM-FV2.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native 1.9x2.5 finite volume grid (96x144 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2-WACCM-FV2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 70 levels; top level 4.5e-6 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 70 levels; top level 4.5e-6 mb); aerosol: MAM4 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 70 levels; top level 4.5e-6 mb); atmosChem: WACCM (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 70 levels; top level 4.5e-6 mb; landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2-WACCM-FV2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC CHEM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/2ebbfd9d-97bf-4858-b893-80d31ffe8cc7</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 historical ensemble with WACCM6-FV2 (1.9x2.5) with middle atmosphere chemistry, interactive land (CLM5, 1.9x2.5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1).</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 36, lat: 96, lon: 144, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time       (time) object 1998-01-15 12:00:00 ... 2000-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "    time_bnds  (time, nbnd) object ...\n",
       "    lat_bnds   (time, lat, nbnd) float64 ...\n",
       "    lon_bnds   (time, lon, nbnd) float64 ...\n",
       "Attributes: (12/45)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_time_in_parent:  10950.0\n",
       "    case_id:                1562\n",
       "    ...                     ...\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               Amon\n",
       "    tracking_id:            hdl:21.14100/2ebbfd9d-97bf-4858-b893-80d31ffe8cc7\n",
       "    variable_id:            tas\n",
       "    variant_info:           CMIP6 CESM2 historical ensemble with WACCM6-FV2 (...\n",
       "    variant_label:          r1i1p1f1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = resp.datasets()[0]\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(resp.provenance_image())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}