ex6.ipynb

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    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import netCDF4 as nc\n",
    "import xarray as xr\n",
    "from scipy.linalg import eig\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.util import add_cyclic_point"
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    "### Compare SLP of observations (GMSLP from former exercises) with PUMA data.\n",
    "\n",
    "Does PUMA reproduce features of observations?\n",
    "\n",
    "To this end, run PUMA for 92 years, take out december months and compare \n",
    "mean(SLP) and first and second EOF for regions:\n",
    "Euroatlantic and Tropics."
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   "source": [
    "path = \"/home/rpinto/Downloads/WS21/KlimaDynamics/Exercises/Klimadynamics/data/\""
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      "KeysView(<xarray.Dataset>\n",
      "Dimensions:  (lat: 32, lev: 1, lon: 64, time: 33120)\n",
      "Coordinates:\n",
      "  * lon      (lon) float64 0.0 5.625 11.25 16.88 ... 337.5 343.1 348.8 354.4\n",
      "  * lat      (lat) float64 85.76 80.27 74.74 69.21 ... -74.74 -80.27 -85.76\n",
      "  * lev      (lev) float64 1e+03\n",
      "  * time     (time) float64 0.0 1.0 2.0 3.0 ... 3.312e+04 3.312e+04 3.312e+04\n",
      "Data variables:\n",
      "    psl      (time, lat, lon) float32 ...\n",
      "Attributes:\n",
      "    title:        PUMA/PLASIM DATA\n",
      "    history:      Created by PumaBurner 7.4\n",
      "    Conventions:  CF-1.0)\n"
     ]
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   ],
   "source": [
    "with xr.open_dataset(path + \"burn_puma_.nc\", decode_times=False) as ds:\n",
    "    print(ds.keys()) #puma simulation for this example has no orography or annual cyclces. "
   ]
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   "source": [
    "dates = xr.cftime_range(start='0001', periods=33120, freq='1D', calendar='360_day') \n",
    "ds = ds.assign_coords(time=('time', dates))"
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   "source": [
    "slp_djf = ds.sel(time=ds['time.season']=='DJF').groupby('time.year').mean('time')"
   ]
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    "tropics = True"
   ]
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    "if tropics:\n",
    "    slp_reg = slp_djf.isel(lat=(slp_djf.lat <= 20) & (slp_djf.lat >= -20))\n",
    "else:\n",
    "    slp_reg = slp_djf.isel(lat=(slp_djf.lat <= 70) & (slp_djf.lat >= 20),\n",
    "                           lon=(slp_djf.lon >= 360-80) | (slp_djf.lon <= 20))"
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       "  * year     (year) int64 1 2 3 4 5 6 7 8 9 10 ... 83 84 85 86 87 88 89 90 91 92\n",
       "Data variables:\n",
       "    psl      (year, lat, lon) float32 1011.9087 1011.9605 ... 1003.2449</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-967dd292-fcd1-4566-adbc-0ffbc326a6ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-967dd292-fcd1-4566-adbc-0ffbc326a6ed' 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'>lat</span>: 8</li><li><span class='xr-has-index'>lev</span>: 1</li><li><span class='xr-has-index'>lon</span>: 64</li><li><span class='xr-has-index'>year</span>: 92</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-431009a2-81ca-44a5-be4c-d7df80d56009' class='xr-section-summary-in' type='checkbox'  checked><label for='section-431009a2-81ca-44a5-be4c-d7df80d56009' 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 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 5.625 11.25 ... 348.8 354.4</div><input id='attrs-2abe2117-83be-4e3f-a047-b3d6cd61e4da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2abe2117-83be-4e3f-a047-b3d6cd61e4da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b89e730-7463-46a8-a44d-4ba239d325c7' class='xr-var-data-in' type='checkbox'><label for='data-9b89e730-7463-46a8-a44d-4ba239d325c7' 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>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</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'>19.38 13.84 8.307 ... -13.84 -19.38</div><input id='attrs-f6562760-4124-4080-9034-af8d6e8c8e9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6562760-4124-4080-9034-af8d6e8c8e9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460e7d6b-de46-4a41-bcdd-7e4d70ceebc9' class='xr-var-data-in' type='checkbox'><label for='data-460e7d6b-de46-4a41-bcdd-7e4d70ceebc9' 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>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 19.382231,  13.844484,   8.306703,   2.768903,  -2.768903,  -8.306703,\n",
       "       -13.844484, -19.382231])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-e47eeb77-3f1f-499b-a1df-4ecddba32b57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e47eeb77-3f1f-499b-a1df-4ecddba32b57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21fd4902-c614-4218-b621-e40834de4c39' class='xr-var-data-in' type='checkbox'><label for='data-21fd4902-c614-4218-b621-e40834de4c39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>pressure</dd><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>array([1000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 87 88 89 90 91 92</div><input id='attrs-be70d1c1-f09b-4a9d-b6ea-20790fd107ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be70d1c1-f09b-4a9d-b6ea-20790fd107ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f13dc358-14da-444e-a2f8-2155a9738c70' class='xr-var-data-in' type='checkbox'><label for='data-f13dc358-14da-444e-a2f8-2155a9738c70' 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,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
       "       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,\n",
       "       73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
       "       91, 92])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23628913-8d8f-4b49-b8e5-2bad918ef1a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23628913-8d8f-4b49-b8e5-2bad918ef1a9' class='xr-section-summary' >Data variables: <span>(1)</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>psl</span></div><div class='xr-var-dims'>(year, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1011.9087 1011.9605 ... 1003.2449</div><input id='attrs-909d02e2-7e2e-47ac-ba7b-28b235af1148' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909d02e2-7e2e-47ac-ba7b-28b235af1148' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef58f53-487d-46a0-86b6-da8b1a311e74' class='xr-var-data-in' type='checkbox'><label for='data-1ef58f53-487d-46a0-86b6-da8b1a311e74' 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([[[1011.9087 , 1011.9605 , 1011.9979 , ..., 1011.8902 ,\n",
       "         1011.8612 , 1011.86884],\n",
       "        [1009.54425, 1009.5641 , 1009.5746 , ..., 1009.53516,\n",
       "         1009.5218 , 1009.52673],\n",
       "        [1007.5144 , 1007.5191 , 1007.5183 , ..., 1007.50903,\n",
       "         1007.50446, 1007.50757],\n",
       "        ...,\n",
       "        [1003.6183 , 1003.6164 , 1003.6133 , ..., 1003.618  ,\n",
       "         1003.61816, 1003.6189 ],\n",
       "        [1002.4293 , 1002.4284 , 1002.4274 , ..., 1002.4328 ,\n",
       "         1002.4319 , 1002.4309 ],\n",
       "        [1001.78687, 1001.7862 , 1001.7868 , ..., 1001.78925,\n",
       "         1001.788  , 1001.78723]],\n",
       "\n",
       "       [[1015.45087, 1015.4429 , 1015.45593, ..., 1015.4912 ,\n",
       "         1015.4833 , 1015.4681 ],\n",
       "        [1012.13257, 1012.1375 , 1012.15607, ..., 1012.1499 ,\n",
       "         1012.14557, 1012.13794],\n",
       "        [1009.60345, 1009.6145 , 1009.6322 , ..., 1009.6054 ,\n",
       "         1009.603  , 1009.60095],\n",
       "...\n",
       "        [1005.2792 , 1005.2803 , 1005.2809 , ..., 1005.27264,\n",
       "         1005.2745 , 1005.2769 ],\n",
       "        [1004.0016 , 1004.0039 , 1004.0039 , ..., 1003.9893 ,\n",
       "         1003.99316, 1003.9977 ],\n",
       "        [1003.25287, 1003.2558 , 1003.2561 , ..., 1003.2387 ,\n",
       "         1003.24304, 1003.24835]],\n",
       "\n",
       "       [[1015.41077, 1015.38464, 1015.3641 , ..., 1015.4274 ,\n",
       "         1015.43805, 1015.4314 ],\n",
       "        [1012.11786, 1012.1028 , 1012.0888 , ..., 1012.12274,\n",
       "         1012.1322 , 1012.1296 ],\n",
       "        [1009.6079 , 1009.5974 , 1009.5874 , ..., 1009.6094 ,\n",
       "         1009.61597, 1009.6145 ],\n",
       "        ...,\n",
       "        [1005.30695, 1005.30225, 1005.2968 , ..., 1005.3076 ,\n",
       "         1005.3097 , 1005.3094 ],\n",
       "        [1004.0088 , 1004.0061 , 1004.00165, ..., 1004.0088 ,\n",
       "         1004.0105 , 1004.0105 ],\n",
       "        [1003.244  , 1003.24225, 1003.23914, ..., 1003.2451 ,\n",
       "         1003.24506, 1003.2449 ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbdbd642-4a51-4500-8739-0549735195af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cbdbd642-4a51-4500-8739-0549735195af' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 8, lev: 1, lon: 64, year: 92)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 5.625 11.25 16.88 ... 337.5 343.1 348.8 354.4\n",
       "  * lat      (lat) float64 19.38 13.84 8.307 2.769 -2.769 -8.307 -13.84 -19.38\n",
       "  * lev      (lev) float64 1e+03\n",
       "  * year     (year) int64 1 2 3 4 5 6 7 8 9 10 ... 83 84 85 86 87 88 89 90 91 92\n",
       "Data variables:\n",
       "    psl      (year, lat, lon) float32 1011.9087 1011.9605 ... 1003.2449"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slp_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:08:02.021725Z",
     "start_time": "2021-01-15T17:08:02.014978Z"
    }
   },
   "outputs": [],
   "source": [
    "n_lat = len(slp_reg.lat)\n",
    "n_lon = len(slp_reg.lon)\n",
    "m = len(slp_reg.year)\n",
    "dim_new = (m, n_lat*n_lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:08:02.383774Z",
     "start_time": "2021-01-15T17:08:02.357956Z"
    }
   },
   "outputs": [],
   "source": [
    "slp_reg_stack = slp_reg.stack(latlon=('lat', 'lon'))\n",
    "\n",
    "# weights\n",
    "W = np.diag(np.sqrt(np.cos(np.deg2rad(slp_reg_stack.latlon.lat))))\n",
    "\n",
    "# calculate covariance matrix\n",
    "# calculate anomalies D and weigh them\n",
    "D = slp_reg_stack - slp_reg_stack.mean('year')\n",
    "D_weighted = D.psl.values @ W\n",
    "\n",
    "cov_mat = (1/m) * D.psl.T.values @ D.psl.values\n",
    "cov_mat_weighted = (1/m) * D_weighted.T @ D_weighted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:08:03.243717Z",
     "start_time": "2021-01-15T17:08:02.841741Z"
    }
   },
   "outputs": [],
   "source": [
    "# PCA\n",
    "# calculate eigenvec and eigenval\n",
    "# column eig_val[:,i] is the eigenvector corresponding to the eigenvalue eig_vec[i]\n",
    "l, E = eig(cov_mat_weighted)  # eigen_val, eigen_vec\n",
    "l = l.real\n",
    "E = E.real\n",
    "\n",
    "ix = np.argsort(l)\n",
    "l = l[ix][::-1]\n",
    "E = E[:, ix][:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:08:04.838820Z",
     "start_time": "2021-01-15T17:08:04.315179Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(7, 5), sharex=True)\n",
    "\n",
    "# plot eigenvalues\n",
    "ax1.plot(range(1, 1+len(l)), l, 'ok-')\n",
    "ax1.set_ylabel('Eigenvalue')\n",
    "ax1.set_xlabel('Number of eigenvalues')\n",
    "\n",
    "# plot explained variance\n",
    "ex_var = np.cumsum(l)/len(l)\n",
    "ax2.plot(range(1, 1+len(l)), ex_var, 'or-')\n",
    "ax2.set_ylabel('Explained variance')\n",
    "ax2.set_xlabel('Number of eigenvalues')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:08:07.805739Z",
     "start_time": "2021-01-15T17:08:07.134890Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.713637689964802 22.713637690515085\n",
      "0.8456398534742122 0.8456398643228538\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot first two eigenvalues\n",
    "fig, [ax1,ax2] = plt.subplots(2, 1, figsize=(10,6))\n",
    "\n",
    "# first and second eigenvector\n",
    "ax1.plot(D.lat.values, E[:, 0], label='First eigenvector')\n",
    "ax1.plot(D.lat.values, E[:, 1], label='Second eigenvector')\n",
    "ax1.legend()\n",
    "ax1.set_xlabel('Latitude')\n",
    "ax1.set_ylabel('Component of eigenvector')\n",
    "\n",
    "# principle components of first and second eigenvector\n",
    "# calculate principle components (A) using the time series as X\n",
    "A = E.T @ D_weighted.T  # principal components\n",
    "\n",
    "for i in range(2):\n",
    "    print(np.var(A[i, :]), l[i])\n",
    "\n",
    "ax2.plot(D.year.values, A[0, :], label='PCA of first eigenvector')\n",
    "ax2.plot(D.year.values, A[1, :], label='PCA of second eigenvector')\n",
    "ax2.legend()\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Principal component')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:04:33.534969Z",
     "start_time": "2021-01-15T17:04:33.083322Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-cc45d9f1a445>:27: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot ith eigenvector on map\n",
    "\n",
    "i = 1 \n",
    "  \n",
    "fig = plt.figure(figsize=(15,15))\n",
    "proj = ccrs.AlbersEqualArea(central_longitude=-30, central_latitude=40, standard_parallels=(0, 80))\n",
    "\n",
    "ax = fig.add_subplot(111, projection=proj)    \n",
    "ax.set_extent([-80, 20, 20, 70], ccrs.PlateCarree())    # set boundary\n",
    "\n",
    "import matplotlib.path as mpath\n",
    "\n",
    "vertices = [(lon, 20) for lon in range(-80, 21, 1)] + \\\n",
    "           [(lon, 70) for lon in range(20, -81, -1)]\n",
    "boundary = mpath.Path(vertices)\n",
    "\n",
    "ax.set_boundary(boundary, transform=ccrs.PlateCarree())    \n",
    "norm = mcolors.TwoSlopeNorm(vmin=np.min(E[:, i-1]), vcenter=0, vmax=np.max(E[:, i-1]))\n",
    "kwargs = dict(shading='auto', norm=norm, cmap='RdBu_r', transform=ccrs.PlateCarree())    \n",
    "\n",
    "#corr_val, ll = add_cyclic_point(corr.T, coord= ds.lon)    \n",
    "im = ax.pcolormesh(slp_reg.lon, slp_reg.lat, E[:, i-1].reshape((n_lat, n_lon))  , **kwargs)    \n",
    "ax.coastlines()\n",
    "#ax.set_title('Eigenvector {}'.format(i))\n",
    "ax.set_global()    \n",
    "fig.colorbar(im, ax=ax, orientation='horizontal', shrink=0.4, label='Component', pad=-0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tropics would not show ENSO as the simulation is a dry only simulation, therefore unable to caputre the latent heat fluxes over the tropics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-15T17:12:44.409604Z",
     "start_time": "2021-01-15T17:12:44.039978Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb62e6d5a30>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%% plot ith eigenvector on map\n",
    "i = 1  # number of eigenvector\n",
    "\n",
    "proj = ccrs.Mollweide(central_longitude=180)\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = fig.add_subplot(111, projection=proj)\n",
    "\n",
    "norm = mcolors.TwoSlopeNorm(vmin=np.min(E[:, i-1]), vcenter=0.05, vmax=np.max(E[:, i-1]))\n",
    "kwargs = dict(shading='nearest', norm=norm, cmap='RdBu_r', transform=ccrs.PlateCarree())\n",
    "\n",
    "new_field, new_lon = add_cyclic_point(E[:, i-1].reshape((n_lat, n_lon)), coord=slp_reg.lon) \n",
    "\n",
    "im = ax.pcolormesh(new_lon, slp_reg.lat, new_field, **kwargs)\n",
    "\n",
    "ax.coastlines()\n",
    "ax.set_title('Eigenvector {}'.format(i))\n",
    "ax.set_global()\n",
    "\n",
    "fig.colorbar(im, ax=ax, orientation='horizontal', shrink=0.5, label='Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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