Bd_pr_CRU.ipynb

{
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   "source": [
    "#writing some conda package or module\n",
    "#!pip install netCDF4\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#!pip install cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.mpl.ticker as cticker\n",
    "from cartopy.util import add_cyclic_point\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eabdcdf5",
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lon: 720, lat: 360, time: 1452)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
       "  * time     (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2021-12-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk</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-bc04179e-a112-4a29-8734-bfa9dd06ccf2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc04179e-a112-4a29-8734-bfa9dd06ccf2' 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'>lon</span>: 720</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>time</span>: 1452</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-917397ef-be6d-4ba9-8f91-c5f571b4d29c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-917397ef-be6d-4ba9-8f91-c5f571b4d29c' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-49164fcc-dc31-47a9-b3cb-cc9b56e978c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49164fcc-dc31-47a9-b3cb-cc9b56e978c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-028a831d-5875-4c45-af16-a7e986c3f6fb' class='xr-var-data-in' type='checkbox'><label for='data-028a831d-5875-4c45-af16-a7e986c3f6fb' 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></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-2269b8a3-f958-403e-828d-0ee3b1896bdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2269b8a3-f958-403e-828d-0ee3b1896bdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44472632-1c27-4907-a64b-1e082cf3159b' class='xr-var-data-in' type='checkbox'><label for='data-44472632-1c27-4907-a64b-1e082cf3159b' 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></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)</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'>1901-01-16 ... 2021-12-16</div><input id='attrs-e7d2f0da-1b22-4760-8b8b-87ba4905010e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7d2f0da-1b22-4760-8b8b-87ba4905010e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa34f96-c4fd-46cb-93be-15a9c6a11a26' class='xr-var-data-in' type='checkbox'><label for='data-caa34f96-c4fd-46cb-93be-15a9c6a11a26' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1901-01-16T00:00:00.000000000&#x27;, &#x27;1901-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1901-03-16T00:00:00.000000000&#x27;, ..., &#x27;2021-10-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff639a67-4dbc-4fb3-82bd-73114df2f643' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff639a67-4dbc-4fb3-82bd-73114df2f643' class='xr-section-summary' >Data variables: <span>(2)</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>pre</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-0ad3bdd6-6544-40d6-a49f-1b570ce2a3d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ad3bdd6-6544-40d6-a49f-1b570ce2a3d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1261e0b-c3ba-4e85-8c37-287f8e945b12' class='xr-var-data-in' type='checkbox'><label for='data-b1261e0b-c3ba-4e85-8c37-287f8e945b12' 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>precipitation</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>correlation_decay_distance :</span></dt><dd>450.0</dd></dl></div><div class='xr-var-data'><pre>[376358400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stn</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-425c4408-4e65-4ce8-a3a8-604153c7fede' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-425c4408-4e65-4ce8-a3a8-604153c7fede' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db0e55c5-5af3-4ae2-9ee7-8d3bb581df0e' class='xr-var-data-in' type='checkbox'><label for='data-db0e55c5-5af3-4ae2-9ee7-8d3bb581df0e' 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>description :</span></dt><dd>number of stations contributing to each datum</dd></dl></div><div class='xr-var-data'><pre>[376358400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98d023a1-aa52-4dd2-8f5e-454620c49586' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98d023a1-aa52-4dd2-8f5e-454620c49586' class='xr-section-summary' >Attributes: <span>(8)</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-1.4</dd><dt><span>title :</span></dt><dd>CRU TS4.06 Precipitation</dd><dt><span>institution :</span></dt><dd>Data held at British Atmospheric Data Centre, RAL, UK.</dd><dt><span>source :</span></dt><dd>Run ID = 2205201912. Data generated from:pre.2204291209.dtb</dd><dt><span>history :</span></dt><dd>Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegridsauto.for called by update.for</dd><dt><span>references :</span></dt><dd>Information on the data is available at http://badc.nerc.ac.uk/data/cru/</dd><dt><span>comment :</span></dt><dd>Access to these data is available to any registered CEDA user.</dd><dt><span>contact :</span></dt><dd>support@ceda.ac.uk</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 720, lat: 360, time: 1452)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
       "  * time     (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2021-12-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/observation/cru/cru_ts4.06.1901.2021.pre.dat.nc')\n",
    " \n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a298a4b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "149f737b",
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       "Dimensions:  (lon: 720, lat: 360, time: 431)\n",
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       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk</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-72703b37-eb72-4503-913e-08ebb701b9de' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72703b37-eb72-4503-913e-08ebb701b9de' 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'>lon</span>: 720</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>time</span>: 431</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-074e0bac-bd16-4726-8835-d2620b7b4bd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-074e0bac-bd16-4726-8835-d2620b7b4bd8' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-c36c7eca-dafe-4b2c-a586-6b810ff17050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c36c7eca-dafe-4b2c-a586-6b810ff17050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bc00b65-e00d-49af-9dd2-c6e36150b535' class='xr-var-data-in' type='checkbox'><label for='data-9bc00b65-e00d-49af-9dd2-c6e36150b535' 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></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-ed2ddf09-18e2-4b0a-bc0b-90b705f06f36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2ddf09-18e2-4b0a-bc0b-90b705f06f36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b54fe656-3204-4a8e-91d9-1287b49795e2' class='xr-var-data-in' type='checkbox'><label for='data-b54fe656-3204-4a8e-91d9-1287b49795e2' 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></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)</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'>1980-02-15 ... 2015-12-16</div><input id='attrs-c87ae822-fff3-4920-a21d-7928f6f56bf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c87ae822-fff3-4920-a21d-7928f6f56bf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-011ef7d3-f942-420f-b8a5-3819399c6cf0' class='xr-var-data-in' type='checkbox'><label for='data-011ef7d3-f942-420f-b8a5-3819399c6cf0' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-02-15T00:00:00.000000000&#x27;, &#x27;1980-03-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-04-16T00:00:00.000000000&#x27;, ..., &#x27;2015-10-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-16T00:00:00.000000000&#x27;, &#x27;2015-12-16T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7877924c-f9e3-4c04-8b41-3eaaff7ba71d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7877924c-f9e3-4c04-8b41-3eaaff7ba71d' class='xr-section-summary' >Data variables: <span>(2)</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>pre</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-e413dbbe-cbfe-4e44-83d1-cab7cd7a24cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e413dbbe-cbfe-4e44-83d1-cab7cd7a24cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-981b98d0-bc91-4a10-b01d-6e4e89a2b38a' class='xr-var-data-in' type='checkbox'><label for='data-981b98d0-bc91-4a10-b01d-6e4e89a2b38a' 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>precipitation</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>correlation_decay_distance :</span></dt><dd>450.0</dd></dl></div><div class='xr-var-data'><pre>[111715200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stn</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-20759349-940a-48ea-b029-0a96c25aba1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20759349-940a-48ea-b029-0a96c25aba1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d1deb66-a7a3-42dc-ae39-37dede075bb6' class='xr-var-data-in' type='checkbox'><label for='data-1d1deb66-a7a3-42dc-ae39-37dede075bb6' 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>description :</span></dt><dd>number of stations contributing to each datum</dd></dl></div><div class='xr-var-data'><pre>[111715200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b34e7fd2-1c40-43d5-84a0-c7f4620e9591' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b34e7fd2-1c40-43d5-84a0-c7f4620e9591' class='xr-section-summary' >Attributes: <span>(8)</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-1.4</dd><dt><span>title :</span></dt><dd>CRU TS4.06 Precipitation</dd><dt><span>institution :</span></dt><dd>Data held at British Atmospheric Data Centre, RAL, UK.</dd><dt><span>source :</span></dt><dd>Run ID = 2205201912. Data generated from:pre.2204291209.dtb</dd><dt><span>history :</span></dt><dd>Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegridsauto.for called by update.for</dd><dt><span>references :</span></dt><dd>Information on the data is available at http://badc.nerc.ac.uk/data/cru/</dd><dt><span>comment :</span></dt><dd>Access to these data is available to any registered CEDA user.</dd><dt><span>contact :</span></dt><dd>support@ceda.ac.uk</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 720, lat: 360, time: 431)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
       "  * time     (time) datetime64[ns] 1980-02-15 1980-03-16 ... 2015-12-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to select the year from 1980 to 2015\n",
    "data = data.sel(time=slice('1980-01-16T12:00:00.000000000','2015-12-16T00:00:00.000000000'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66f463e9",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\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.Dataset&gt;\n",
       "Dimensions:  (lon: 720, lat: 360, time: 144)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
       "  * time     (time) datetime64[ns] 1980-06-16 1980-07-16 ... 2015-09-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk</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-87bad124-b0bf-4d20-8dac-76e96a394b58' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87bad124-b0bf-4d20-8dac-76e96a394b58' 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'>lon</span>: 720</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>time</span>: 144</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8cf29d35-5ffe-4984-915d-f97ccada4353' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cf29d35-5ffe-4984-915d-f97ccada4353' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-c4340859-6ddc-4abd-8d7b-8fcf60085064' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4340859-6ddc-4abd-8d7b-8fcf60085064' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be385a46-da33-426c-b9f9-7338261c0ba8' class='xr-var-data-in' type='checkbox'><label for='data-be385a46-da33-426c-b9f9-7338261c0ba8' 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></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-031b3f0d-696c-4b13-a1ac-3a62eceb2524' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-031b3f0d-696c-4b13-a1ac-3a62eceb2524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20821a6b-63f7-41e5-9c35-866bc6e01bb8' class='xr-var-data-in' type='checkbox'><label for='data-20821a6b-63f7-41e5-9c35-866bc6e01bb8' 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></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)</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'>1980-06-16 ... 2015-09-16</div><input id='attrs-674b0527-1961-4ed7-ade3-28592c95743b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-674b0527-1961-4ed7-ade3-28592c95743b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e63ddb29-080a-4ea1-81af-cd8d40cd0970' class='xr-var-data-in' type='checkbox'><label for='data-e63ddb29-080a-4ea1-81af-cd8d40cd0970' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-06-16T00:00:00.000000000&#x27;, &#x27;1980-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-08-16T00:00:00.000000000&#x27;, &#x27;1980-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-06-16T00:00:00.000000000&#x27;, &#x27;1981-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-08-16T00:00:00.000000000&#x27;, &#x27;1981-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-06-16T00:00:00.000000000&#x27;, &#x27;1982-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-08-16T00:00:00.000000000&#x27;, &#x27;1982-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-06-16T00:00:00.000000000&#x27;, &#x27;1983-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-08-16T00:00:00.000000000&#x27;, &#x27;1983-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-06-16T00:00:00.000000000&#x27;, &#x27;1984-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-08-16T00:00:00.000000000&#x27;, &#x27;1984-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-06-16T00:00:00.000000000&#x27;, &#x27;1985-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-08-16T00:00:00.000000000&#x27;, &#x27;1985-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-06-16T00:00:00.000000000&#x27;, &#x27;1986-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-08-16T00:00:00.000000000&#x27;, &#x27;1986-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-06-16T00:00:00.000000000&#x27;, &#x27;1987-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-08-16T00:00:00.000000000&#x27;, &#x27;1987-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-06-16T00:00:00.000000000&#x27;, &#x27;1988-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-08-16T00:00:00.000000000&#x27;, &#x27;1988-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-06-16T00:00:00.000000000&#x27;, &#x27;1989-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-08-16T00:00:00.000000000&#x27;, &#x27;1989-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-06-16T00:00:00.000000000&#x27;, &#x27;1990-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-08-16T00:00:00.000000000&#x27;, &#x27;1990-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-06-16T00:00:00.000000000&#x27;, &#x27;1991-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-08-16T00:00:00.000000000&#x27;, &#x27;1991-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-06-16T00:00:00.000000000&#x27;, &#x27;1992-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-08-16T00:00:00.000000000&#x27;, &#x27;1992-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-06-16T00:00:00.000000000&#x27;, &#x27;1993-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-08-16T00:00:00.000000000&#x27;, &#x27;1993-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-06-16T00:00:00.000000000&#x27;, &#x27;1994-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-08-16T00:00:00.000000000&#x27;, &#x27;1994-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-06-16T00:00:00.000000000&#x27;, &#x27;1995-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-08-16T00:00:00.000000000&#x27;, &#x27;1995-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-06-16T00:00:00.000000000&#x27;, &#x27;1996-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-08-16T00:00:00.000000000&#x27;, &#x27;1996-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-06-16T00:00:00.000000000&#x27;, &#x27;1997-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-08-16T00:00:00.000000000&#x27;, &#x27;1997-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-06-16T00:00:00.000000000&#x27;, &#x27;1998-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-08-16T00:00:00.000000000&#x27;, &#x27;1998-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-06-16T00:00:00.000000000&#x27;, &#x27;1999-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-08-16T00:00:00.000000000&#x27;, &#x27;1999-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-06-16T00:00:00.000000000&#x27;, &#x27;2000-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-08-16T00:00:00.000000000&#x27;, &#x27;2000-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-06-16T00:00:00.000000000&#x27;, &#x27;2001-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-08-16T00:00:00.000000000&#x27;, &#x27;2001-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-06-16T00:00:00.000000000&#x27;, &#x27;2002-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-08-16T00:00:00.000000000&#x27;, &#x27;2002-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-06-16T00:00:00.000000000&#x27;, &#x27;2003-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-08-16T00:00:00.000000000&#x27;, &#x27;2003-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-06-16T00:00:00.000000000&#x27;, &#x27;2004-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-08-16T00:00:00.000000000&#x27;, &#x27;2004-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-06-16T00:00:00.000000000&#x27;, &#x27;2005-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-16T00:00:00.000000000&#x27;, &#x27;2005-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-06-16T00:00:00.000000000&#x27;, &#x27;2006-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-08-16T00:00:00.000000000&#x27;, &#x27;2006-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-06-16T00:00:00.000000000&#x27;, &#x27;2007-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-08-16T00:00:00.000000000&#x27;, &#x27;2007-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-06-16T00:00:00.000000000&#x27;, &#x27;2008-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-08-16T00:00:00.000000000&#x27;, &#x27;2008-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-06-16T00:00:00.000000000&#x27;, &#x27;2009-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-08-16T00:00:00.000000000&#x27;, &#x27;2009-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-06-16T00:00:00.000000000&#x27;, &#x27;2010-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-08-16T00:00:00.000000000&#x27;, &#x27;2010-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-06-16T00:00:00.000000000&#x27;, &#x27;2011-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-08-16T00:00:00.000000000&#x27;, &#x27;2011-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-16T00:00:00.000000000&#x27;, &#x27;2012-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-08-16T00:00:00.000000000&#x27;, &#x27;2012-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-06-16T00:00:00.000000000&#x27;, &#x27;2013-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-08-16T00:00:00.000000000&#x27;, &#x27;2013-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-06-16T00:00:00.000000000&#x27;, &#x27;2014-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-08-16T00:00:00.000000000&#x27;, &#x27;2014-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-06-16T00:00:00.000000000&#x27;, &#x27;2015-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-16T00:00:00.000000000&#x27;, &#x27;2015-09-16T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2bac598-6ac9-4940-b3ef-e6cd4347b07c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2bac598-6ac9-4940-b3ef-e6cd4347b07c' class='xr-section-summary' >Data variables: <span>(2)</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>pre</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-140e6d7b-37c1-461f-ae20-9e835bbad0ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-140e6d7b-37c1-461f-ae20-9e835bbad0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a075e1ce-0ba4-43cd-96b6-19a220480b8f' class='xr-var-data-in' type='checkbox'><label for='data-a075e1ce-0ba4-43cd-96b6-19a220480b8f' 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>precipitation</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>correlation_decay_distance :</span></dt><dd>450.0</dd></dl></div><div class='xr-var-data'><pre>[37324800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stn</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48c9cdf5-63e4-464f-ba18-a3d10cdb1b1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48c9cdf5-63e4-464f-ba18-a3d10cdb1b1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b5020d-688d-45e6-a83c-01a68719e844' class='xr-var-data-in' type='checkbox'><label for='data-44b5020d-688d-45e6-a83c-01a68719e844' 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>description :</span></dt><dd>number of stations contributing to each datum</dd></dl></div><div class='xr-var-data'><pre>[37324800 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d42bfbcc-f308-499e-b3b8-5bd5554dae49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d42bfbcc-f308-499e-b3b8-5bd5554dae49' class='xr-section-summary' >Attributes: <span>(8)</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-1.4</dd><dt><span>title :</span></dt><dd>CRU TS4.06 Precipitation</dd><dt><span>institution :</span></dt><dd>Data held at British Atmospheric Data Centre, RAL, UK.</dd><dt><span>source :</span></dt><dd>Run ID = 2205201912. Data generated from:pre.2204291209.dtb</dd><dt><span>history :</span></dt><dd>Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegridsauto.for called by update.for</dd><dt><span>references :</span></dt><dd>Information on the data is available at http://badc.nerc.ac.uk/data/cru/</dd><dt><span>comment :</span></dt><dd>Access to these data is available to any registered CEDA user.</dd><dt><span>contact :</span></dt><dd>support@ceda.ac.uk</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 720, lat: 360, time: 144)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
       "  * time     (time) datetime64[ns] 1980-06-16 1980-07-16 ... 2015-09-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting only JJAS months\n",
    "\n",
    "def is_jjas(month):\n",
    "    return (month >= 6) & (month <= 9)\n",
    "\n",
    "jjas_data = data.sel(time=is_jjas(data['time.month']))\n",
    "\n",
    "jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b40f47b",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lon: 22, lat: 22, time: 144)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 85.25 85.75 86.25 86.75 ... 94.25 94.75 95.25 95.75\n",
       "  * lat      (lat) float32 18.25 18.75 19.25 19.75 ... 27.25 27.75 28.25 28.75\n",
       "  * time     (time) datetime64[ns] 1980-06-16 1980-07-16 ... 2015-09-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 nan nan nan nan ... 82.0 129.3 108.2 110.7\n",
       "    stn      (time, lat, lon) float64 nan nan nan nan nan ... 5.0 6.0 6.0 5.0\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk</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-d92d450d-a153-4511-834e-10f7ee074e2e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d92d450d-a153-4511-834e-10f7ee074e2e' 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'>lon</span>: 22</li><li><span class='xr-has-index'>lat</span>: 22</li><li><span class='xr-has-index'>time</span>: 144</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-67aab265-119c-40f9-96d8-2dbbc05d78a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67aab265-119c-40f9-96d8-2dbbc05d78a3' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>85.25 85.75 86.25 ... 95.25 95.75</div><input id='attrs-18d88b7d-d2f6-4ee6-a210-d1920d11bc85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18d88b7d-d2f6-4ee6-a210-d1920d11bc85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daf27107-7174-4b4c-8c7a-a098f169782b' class='xr-var-data-in' type='checkbox'><label for='data-daf27107-7174-4b4c-8c7a-a098f169782b' 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></dl></div><div class='xr-var-data'><pre>array([85.25, 85.75, 86.25, 86.75, 87.25, 87.75, 88.25, 88.75, 89.25, 89.75,\n",
       "       90.25, 90.75, 91.25, 91.75, 92.25, 92.75, 93.25, 93.75, 94.25, 94.75,\n",
       "       95.25, 95.75], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>18.25 18.75 19.25 ... 28.25 28.75</div><input id='attrs-8c17f157-5a55-4146-a64b-af17243514c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c17f157-5a55-4146-a64b-af17243514c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a61903f-1ef1-450e-9567-aa8a3a6c95c3' class='xr-var-data-in' type='checkbox'><label for='data-1a61903f-1ef1-450e-9567-aa8a3a6c95c3' 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></dl></div><div class='xr-var-data'><pre>array([18.25, 18.75, 19.25, 19.75, 20.25, 20.75, 21.25, 21.75, 22.25, 22.75,\n",
       "       23.25, 23.75, 24.25, 24.75, 25.25, 25.75, 26.25, 26.75, 27.25, 27.75,\n",
       "       28.25, 28.75], dtype=float32)</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'>1980-06-16 ... 2015-09-16</div><input id='attrs-e20a5c24-e5a1-4fcd-895a-65d41a42a5cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e20a5c24-e5a1-4fcd-895a-65d41a42a5cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d78d5763-2a16-441a-9dd6-b776946a5e4f' class='xr-var-data-in' type='checkbox'><label for='data-d78d5763-2a16-441a-9dd6-b776946a5e4f' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-06-16T00:00:00.000000000&#x27;, &#x27;1980-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-08-16T00:00:00.000000000&#x27;, &#x27;1980-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-06-16T00:00:00.000000000&#x27;, &#x27;1981-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-08-16T00:00:00.000000000&#x27;, &#x27;1981-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-06-16T00:00:00.000000000&#x27;, &#x27;1982-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-08-16T00:00:00.000000000&#x27;, &#x27;1982-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-06-16T00:00:00.000000000&#x27;, &#x27;1983-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-08-16T00:00:00.000000000&#x27;, &#x27;1983-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-06-16T00:00:00.000000000&#x27;, &#x27;1984-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-08-16T00:00:00.000000000&#x27;, &#x27;1984-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-06-16T00:00:00.000000000&#x27;, &#x27;1985-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-08-16T00:00:00.000000000&#x27;, &#x27;1985-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-06-16T00:00:00.000000000&#x27;, &#x27;1986-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-08-16T00:00:00.000000000&#x27;, &#x27;1986-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-06-16T00:00:00.000000000&#x27;, &#x27;1987-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-08-16T00:00:00.000000000&#x27;, &#x27;1987-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-06-16T00:00:00.000000000&#x27;, &#x27;1988-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-08-16T00:00:00.000000000&#x27;, &#x27;1988-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-06-16T00:00:00.000000000&#x27;, &#x27;1989-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-08-16T00:00:00.000000000&#x27;, &#x27;1989-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-06-16T00:00:00.000000000&#x27;, &#x27;1990-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-08-16T00:00:00.000000000&#x27;, &#x27;1990-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-06-16T00:00:00.000000000&#x27;, &#x27;1991-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-08-16T00:00:00.000000000&#x27;, &#x27;1991-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-06-16T00:00:00.000000000&#x27;, &#x27;1992-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-08-16T00:00:00.000000000&#x27;, &#x27;1992-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-06-16T00:00:00.000000000&#x27;, &#x27;1993-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-08-16T00:00:00.000000000&#x27;, &#x27;1993-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-06-16T00:00:00.000000000&#x27;, &#x27;1994-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-08-16T00:00:00.000000000&#x27;, &#x27;1994-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-06-16T00:00:00.000000000&#x27;, &#x27;1995-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-08-16T00:00:00.000000000&#x27;, &#x27;1995-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-06-16T00:00:00.000000000&#x27;, &#x27;1996-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-08-16T00:00:00.000000000&#x27;, &#x27;1996-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-06-16T00:00:00.000000000&#x27;, &#x27;1997-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-08-16T00:00:00.000000000&#x27;, &#x27;1997-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-06-16T00:00:00.000000000&#x27;, &#x27;1998-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-08-16T00:00:00.000000000&#x27;, &#x27;1998-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-06-16T00:00:00.000000000&#x27;, &#x27;1999-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-08-16T00:00:00.000000000&#x27;, &#x27;1999-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-06-16T00:00:00.000000000&#x27;, &#x27;2000-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-08-16T00:00:00.000000000&#x27;, &#x27;2000-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-06-16T00:00:00.000000000&#x27;, &#x27;2001-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-08-16T00:00:00.000000000&#x27;, &#x27;2001-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-06-16T00:00:00.000000000&#x27;, &#x27;2002-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-08-16T00:00:00.000000000&#x27;, &#x27;2002-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-06-16T00:00:00.000000000&#x27;, &#x27;2003-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-08-16T00:00:00.000000000&#x27;, &#x27;2003-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-06-16T00:00:00.000000000&#x27;, &#x27;2004-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-08-16T00:00:00.000000000&#x27;, &#x27;2004-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-06-16T00:00:00.000000000&#x27;, &#x27;2005-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-16T00:00:00.000000000&#x27;, &#x27;2005-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-06-16T00:00:00.000000000&#x27;, &#x27;2006-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-08-16T00:00:00.000000000&#x27;, &#x27;2006-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-06-16T00:00:00.000000000&#x27;, &#x27;2007-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-08-16T00:00:00.000000000&#x27;, &#x27;2007-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-06-16T00:00:00.000000000&#x27;, &#x27;2008-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-08-16T00:00:00.000000000&#x27;, &#x27;2008-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-06-16T00:00:00.000000000&#x27;, &#x27;2009-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-08-16T00:00:00.000000000&#x27;, &#x27;2009-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-06-16T00:00:00.000000000&#x27;, &#x27;2010-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-08-16T00:00:00.000000000&#x27;, &#x27;2010-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-06-16T00:00:00.000000000&#x27;, &#x27;2011-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-08-16T00:00:00.000000000&#x27;, &#x27;2011-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-16T00:00:00.000000000&#x27;, &#x27;2012-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-08-16T00:00:00.000000000&#x27;, &#x27;2012-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-06-16T00:00:00.000000000&#x27;, &#x27;2013-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-08-16T00:00:00.000000000&#x27;, &#x27;2013-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-06-16T00:00:00.000000000&#x27;, &#x27;2014-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-08-16T00:00:00.000000000&#x27;, &#x27;2014-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-06-16T00:00:00.000000000&#x27;, &#x27;2015-07-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-16T00:00:00.000000000&#x27;, &#x27;2015-09-16T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c04655e3-68a9-4c4c-886f-5861cd305af4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c04655e3-68a9-4c4c-886f-5861cd305af4' class='xr-section-summary' >Data variables: <span>(2)</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>pre</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-0c3cf2f2-c3fe-4fe8-bd94-f12a1636d11d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c3cf2f2-c3fe-4fe8-bd94-f12a1636d11d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5be597b4-b447-47ce-bc61-41a8c6972549' class='xr-var-data-in' type='checkbox'><label for='data-5be597b4-b447-47ce-bc61-41a8c6972549' 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>precipitation</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>correlation_decay_distance :</span></dt><dd>450.0</dd></dl></div><div class='xr-var-data'><pre>array([[[       nan,        nan, ..., 772.10004 , 516.10004 ],\n",
       "        [       nan,        nan, ..., 611.7     , 457.30002 ],\n",
       "        ...,\n",
       "        [153.6     ,  48.9     , ..., 247.2     , 233.7     ],\n",
       "        [ 29.7     ,  26.300001, ..., 148.8     , 146.7     ]],\n",
       "\n",
       "       [[       nan,        nan, ..., 744.60004 , 579.10004 ],\n",
       "        [       nan,        nan, ..., 690.      , 546.5     ],\n",
       "        ...,\n",
       "        [201.7     ,  49.2     , ..., 407.      , 377.80002 ],\n",
       "        [ 34.7     ,  30.6     , ..., 240.7     , 229.6     ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[       nan,        nan, ..., 564.7     , 419.80002 ],\n",
       "        [       nan,        nan, ..., 345.5     , 306.2     ],\n",
       "        ...,\n",
       "        [256.9     ,  84.3     , ..., 488.80002 , 475.4     ],\n",
       "        [ 72.9     ,  68.3     , ..., 289.      , 299.1     ]],\n",
       "\n",
       "       [[       nan,        nan, ..., 360.1     , 294.4     ],\n",
       "        [       nan,        nan, ..., 304.1     , 265.5     ],\n",
       "        ...,\n",
       "        [ 67.5     ,  19.6     , ..., 161.1     , 161.      ],\n",
       "        [ 14.2     ,  11.2     , ..., 108.200005, 110.700005]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stn</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-399d9c5b-b6ca-4839-9d42-2b7eb38c58ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-399d9c5b-b6ca-4839-9d42-2b7eb38c58ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02578397-a24a-420d-98d3-6302ceda04f0' class='xr-var-data-in' type='checkbox'><label for='data-02578397-a24a-420d-98d3-6302ceda04f0' 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>description :</span></dt><dd>number of stations contributing to each datum</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ...,  8.,  8.],\n",
       "        [nan, nan, ...,  8.,  8.],\n",
       "        ...,\n",
       "        [ 8.,  8., ...,  8.,  8.],\n",
       "        [ 8.,  8., ...,  8.,  8.]],\n",
       "\n",
       "       [[nan, nan, ...,  7.,  8.],\n",
       "        [nan, nan, ...,  6.,  8.],\n",
       "        ...,\n",
       "        [ 8.,  8., ...,  8.,  8.],\n",
       "        [ 8.,  8., ...,  8.,  8.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[nan, nan, ...,  1.,  1.],\n",
       "        [nan, nan, ...,  1.,  1.],\n",
       "        ...,\n",
       "        [ 2.,  3., ...,  6.,  5.],\n",
       "        [ 2.,  2., ...,  5.,  4.]],\n",
       "\n",
       "       [[nan, nan, ...,  4.,  4.],\n",
       "        [nan, nan, ...,  4.,  4.],\n",
       "        ...,\n",
       "        [ 2.,  3., ...,  7.,  6.],\n",
       "        [ 2.,  2., ...,  6.,  5.]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3df14c33-e68a-424c-bbe3-f839059697aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3df14c33-e68a-424c-bbe3-f839059697aa' class='xr-section-summary' >Attributes: <span>(8)</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-1.4</dd><dt><span>title :</span></dt><dd>CRU TS4.06 Precipitation</dd><dt><span>institution :</span></dt><dd>Data held at British Atmospheric Data Centre, RAL, UK.</dd><dt><span>source :</span></dt><dd>Run ID = 2205201912. Data generated from:pre.2204291209.dtb</dd><dt><span>history :</span></dt><dd>Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegridsauto.for called by update.for</dd><dt><span>references :</span></dt><dd>Information on the data is available at http://badc.nerc.ac.uk/data/cru/</dd><dt><span>comment :</span></dt><dd>Access to these data is available to any registered CEDA user.</dd><dt><span>contact :</span></dt><dd>support@ceda.ac.uk</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lon: 22, lat: 22, time: 144)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 85.25 85.75 86.25 86.75 ... 94.25 94.75 95.25 95.75\n",
       "  * lat      (lat) float32 18.25 18.75 19.25 19.75 ... 27.25 27.75 28.25 28.75\n",
       "  * time     (time) datetime64[ns] 1980-06-16 1980-07-16 ... 2015-09-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.06 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2205201912. Data generated from:pre.2204291209.dtb\n",
       "    history:      Fri 20 May 21:52:05 BST 2022 : User f098 : Program makegrid...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to select the x and y coordinates\n",
    "jjas_data = jjas_data.sel(lon=slice(85,96),lat=slice(18,29))\n",
    "jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8ca89be",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pre&#x27; (lat: 22, lon: 22)&gt;\n",
       "array([[        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan, 1034.6515  ,  775.7047  ,\n",
       "         567.8445  ,  459.40826 ],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,  970.71735 ,  954.46246 ,  706.0424  ,\n",
       "         436.22437 ,  377.15414 ],\n",
       "       [ 210.31253 ,         nan,         nan,         nan,         nan,\n",
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       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,  877.9054  ,  795.74817 ,  610.2514  ,\n",
       "         410.73526 ,  331.86877 ],\n",
       "       [ 233.51044 ,  254.16315 ,  277.49377 ,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,  838.787   ,  777.1486  ,  673.3811  ,  528.67017 ,\n",
       "         379.309   ,  287.37512 ],\n",
       "...\n",
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       "         445.82367 ,  480.15137 ,  491.53278 ,  424.8117  ,  465.67584 ,\n",
       "         506.3611  ,  469.05002 ,  422.99585 ,  294.42294 ,  227.77773 ,\n",
       "         261.24786 ,  245.58821 ,  336.3438  ,  435.31946 ,  404.5549  ,\n",
       "         401.0619  ,  361.26044 ],\n",
       "       [ 393.6672  ,  396.11728 ,  268.32498 ,  134.91046 ,  226.42015 ,\n",
       "         144.15    ,   99.29515 ,  139.73196 ,   87.101395,  258.51804 ,\n",
       "         199.36388 ,  174.93124 ,  231.5514  ,  143.13126 ,  100.15139 ,\n",
       "         141.65282 ,  196.62294 ,  257.96466 ,  340.24655 ,  385.6369  ,\n",
       "         394.65125 ,  365.109   ],\n",
       "       [ 156.01459 ,   45.50694 ,   36.967365,   34.52291 ,   49.522915,\n",
       "          51.65069 ,   52.630554,   36.815968,   41.601387,   49.157635,\n",
       "          52.256252,   64.30278 ,   72.08334 ,   59.42014 ,   60.440964,\n",
       "          71.535385,  129.05139 ,  184.83893 ,  231.33195 ,  314.77997 ,\n",
       "         301.82294 ,  292.72098 ],\n",
       "       [  34.581947,   31.837494,   33.7132  ,   37.455563,   38.499996,\n",
       "          44.835415,   36.288895,   36.14515 ,   36.405567,   38.515965,\n",
       "          38.106247,   48.795837,   47.681244,   47.85    ,   51.85487 ,\n",
       "          61.334026,   75.93472 ,   96.22846 ,  136.584   ,  229.94524 ,\n",
       "         184.72221 ,  186.31526 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 85.25 85.75 86.25 86.75 ... 94.25 94.75 95.25 95.75\n",
       "  * lat      (lat) float32 18.25 18.75 19.25 19.75 ... 27.25 27.75 28.25 28.75</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 22</li><li><span class='xr-has-index'>lon</span>: 22</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c836298b-2cb7-4a97-a607-9e2e7d70d007' class='xr-array-in' type='checkbox' checked><label for='section-c836298b-2cb7-4a97-a607-9e2e7d70d007' 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>nan nan nan nan nan nan nan ... 75.93 96.23 136.6 229.9 184.7 186.3</span></div><div class='xr-array-data'><pre>array([[        nan,         nan,         nan,         nan,         nan,\n",
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       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,  970.71735 ,  954.46246 ,  706.0424  ,\n",
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       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,  877.9054  ,  795.74817 ,  610.2514  ,\n",
       "         410.73526 ,  331.86877 ],\n",
       "       [ 233.51044 ,  254.16315 ,  277.49377 ,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,  838.787   ,  777.1486  ,  673.3811  ,  528.67017 ,\n",
       "         379.309   ,  287.37512 ],\n",
       "...\n",
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       "         445.82367 ,  480.15137 ,  491.53278 ,  424.8117  ,  465.67584 ,\n",
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       "         261.24786 ,  245.58821 ,  336.3438  ,  435.31946 ,  404.5549  ,\n",
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       "         394.65125 ,  365.109   ],\n",
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       "          51.65069 ,   52.630554,   36.815968,   41.601387,   49.157635,\n",
       "          52.256252,   64.30278 ,   72.08334 ,   59.42014 ,   60.440964,\n",
       "          71.535385,  129.05139 ,  184.83893 ,  231.33195 ,  314.77997 ,\n",
       "         301.82294 ,  292.72098 ],\n",
       "       [  34.581947,   31.837494,   33.7132  ,   37.455563,   38.499996,\n",
       "          44.835415,   36.288895,   36.14515 ,   36.405567,   38.515965,\n",
       "          38.106247,   48.795837,   47.681244,   47.85    ,   51.85487 ,\n",
       "          61.334026,   75.93472 ,   96.22846 ,  136.584   ,  229.94524 ,\n",
       "         184.72221 ,  186.31526 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f852591e-a394-4240-b9a9-b6aafab37d1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f852591e-a394-4240-b9a9-b6aafab37d1b' class='xr-section-summary' >Coordinates: <span>(2)</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'>float32</div><div class='xr-var-preview xr-preview'>85.25 85.75 86.25 ... 95.25 95.75</div><input id='attrs-868641c5-d44c-4075-a13d-35200c379957' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-868641c5-d44c-4075-a13d-35200c379957' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f587c42a-4e6d-4a0f-adc5-159a5f093409' class='xr-var-data-in' type='checkbox'><label for='data-f587c42a-4e6d-4a0f-adc5-159a5f093409' 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></dl></div><div class='xr-var-data'><pre>array([85.25, 85.75, 86.25, 86.75, 87.25, 87.75, 88.25, 88.75, 89.25, 89.75,\n",
       "       90.25, 90.75, 91.25, 91.75, 92.25, 92.75, 93.25, 93.75, 94.25, 94.75,\n",
       "       95.25, 95.75], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>18.25 18.75 19.25 ... 28.25 28.75</div><input id='attrs-9451aef1-717f-420e-8c2d-978ab87c3006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9451aef1-717f-420e-8c2d-978ab87c3006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbdf6f64-7cd8-468f-b397-58c38ee5a53a' class='xr-var-data-in' type='checkbox'><label for='data-cbdf6f64-7cd8-468f-b397-58c38ee5a53a' 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></dl></div><div class='xr-var-data'><pre>array([18.25, 18.75, 19.25, 19.75, 20.25, 20.75, 21.25, 21.75, 22.25, 22.75,\n",
       "       23.25, 23.75, 24.25, 24.75, 25.25, 25.75, 26.25, 26.75, 27.25, 27.75,\n",
       "       28.25, 28.75], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-abd1d81e-8454-4675-942f-db3e5d40334c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abd1d81e-8454-4675-942f-db3e5d40334c' 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>"
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       "<xarray.DataArray 'pre' (lat: 22, lon: 22)>\n",
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       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan, 1034.6515  ,  775.7047  ,\n",
       "         567.8445  ,  459.40826 ],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,  970.71735 ,  954.46246 ,  706.0424  ,\n",
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       "       [ 210.31253 ,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
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       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,  838.787   ,  777.1486  ,  673.3811  ,  528.67017 ,\n",
       "         379.309   ,  287.37512 ],\n",
       "...\n",
       "       [ 394.49106 ,  357.5507  ,  311.65414 ,  370.85568 ,  409.8978  ,\n",
       "         445.82367 ,  480.15137 ,  491.53278 ,  424.8117  ,  465.67584 ,\n",
       "         506.3611  ,  469.05002 ,  422.99585 ,  294.42294 ,  227.77773 ,\n",
       "         261.24786 ,  245.58821 ,  336.3438  ,  435.31946 ,  404.5549  ,\n",
       "         401.0619  ,  361.26044 ],\n",
       "       [ 393.6672  ,  396.11728 ,  268.32498 ,  134.91046 ,  226.42015 ,\n",
       "         144.15    ,   99.29515 ,  139.73196 ,   87.101395,  258.51804 ,\n",
       "         199.36388 ,  174.93124 ,  231.5514  ,  143.13126 ,  100.15139 ,\n",
       "         141.65282 ,  196.62294 ,  257.96466 ,  340.24655 ,  385.6369  ,\n",
       "         394.65125 ,  365.109   ],\n",
       "       [ 156.01459 ,   45.50694 ,   36.967365,   34.52291 ,   49.522915,\n",
       "          51.65069 ,   52.630554,   36.815968,   41.601387,   49.157635,\n",
       "          52.256252,   64.30278 ,   72.08334 ,   59.42014 ,   60.440964,\n",
       "          71.535385,  129.05139 ,  184.83893 ,  231.33195 ,  314.77997 ,\n",
       "         301.82294 ,  292.72098 ],\n",
       "       [  34.581947,   31.837494,   33.7132  ,   37.455563,   38.499996,\n",
       "          44.835415,   36.288895,   36.14515 ,   36.405567,   38.515965,\n",
       "          38.106247,   48.795837,   47.681244,   47.85    ,   51.85487 ,\n",
       "          61.334026,   75.93472 ,   96.22846 ,  136.584   ,  229.94524 ,\n",
       "         184.72221 ,  186.31526 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 85.25 85.75 86.25 86.75 ... 94.25 94.75 95.25 95.75\n",
       "  * lat      (lat) float32 18.25 18.75 19.25 19.75 ... 27.25 27.75 28.25 28.75"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the mean for JJAS data\n",
    "jjas_mean = jjas_data.pre.mean('time')\n",
    " \n",
    "jjas_mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2eab95c7",
   "metadata": {},
   "outputs": [
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       "  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;pre&#x27; (lat: 22, lon: 22)&gt;\n",
       "array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan, 34.015903 , 25.502592 ,\n",
       "        18.668839 , 15.103817 ],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 31.91396  , 31.379553 , 23.212328 ,\n",
       "        14.341607 , 12.399574 ],\n",
       "       [ 6.9143767,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 28.862612 , 26.161554 , 20.063038 ,\n",
       "        13.50361  , 10.910742 ],\n",
       "       [ 7.677047 ,  8.356039 ,  9.123073 ,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 27.576529 , 25.550064 , 22.138533 , 17.380919 ,\n",
       "        12.470419 ,  9.447939 ],\n",
       "...\n",
       "       [12.969555 , 11.755078 , 10.246152 , 12.192502 , 13.476077 ,\n",
       "        14.6572   , 15.785781 , 16.159964 , 13.966396 , 15.309874 ,\n",
       "        16.64747  , 15.420805 , 13.906697 ,  9.679647 ,  7.4885745,\n",
       "         8.588962 ,  8.074124 , 11.057866 , 14.311856 , 13.300421 ,\n",
       "        13.185582 , 11.877043 ],\n",
       "       [12.94247  , 13.02302  ,  8.821633 ,  4.4354076,  7.443942 ,\n",
       "         4.7391725,  3.2644944,  4.5939226,  2.8636043,  8.499213 ,\n",
       "         6.5544214,  5.7511578,  7.6126404,  4.70568  ,  3.2926447,\n",
       "         4.657074 ,  6.4643087,  8.481021 , 11.186175 , 12.678459 ,\n",
       "        12.974821 , 12.003571 ],\n",
       "       [ 5.129241 ,  1.4961169,  1.2153641,  1.1349986,  1.6281488,\n",
       "         1.698103 ,  1.7303177,  1.2103866,  1.3677154,  1.6161397,\n",
       "         1.7180119,  2.1140616,  2.3698606,  1.9535366,  1.987098 ,\n",
       "         2.3518457,  4.2427807,  6.0768895,  7.605426 , 10.348919 ,\n",
       "         9.922935 ,  9.6236925],\n",
       "       [ 1.1369395,  1.046711 ,  1.1083779,  1.2314144,  1.2657518,\n",
       "         1.4740394,  1.1930583,  1.1883323,  1.196894 ,  1.2662768,\n",
       "         1.2528068,  1.604245 ,  1.5676008,  1.5731488,  1.7048157,\n",
       "         2.016459 ,  2.4964812,  3.163672 ,  4.490428 ,  7.559835 ,\n",
       "         6.0730524,  6.1254263]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 85.25 85.75 86.25 86.75 ... 94.25 94.75 95.25 95.75\n",
       "  * lat      (lat) float32 18.25 18.75 19.25 19.75 ... 27.25 27.75 28.25 28.75</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 22</li><li><span class='xr-has-index'>lon</span>: 22</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b271dc52-7f51-4509-a4f0-3e5451c10184' class='xr-array-in' type='checkbox' checked><label for='section-b271dc52-7f51-4509-a4f0-3e5451c10184' 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>nan nan nan nan nan nan nan ... 2.496 3.164 4.49 7.56 6.073 6.125</span></div><div class='xr-array-data'><pre>array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan, 34.015903 , 25.502592 ,\n",
       "        18.668839 , 15.103817 ],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 31.91396  , 31.379553 , 23.212328 ,\n",
       "        14.341607 , 12.399574 ],\n",
       "       [ 6.9143767,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 28.862612 , 26.161554 , 20.063038 ,\n",
       "        13.50361  , 10.910742 ],\n",
       "       [ 7.677047 ,  8.356039 ,  9.123073 ,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 27.576529 , 25.550064 , 22.138533 , 17.380919 ,\n",
       "        12.470419 ,  9.447939 ],\n",
       "...\n",
       "       [12.969555 , 11.755078 , 10.246152 , 12.192502 , 13.476077 ,\n",
       "        14.6572   , 15.785781 , 16.159964 , 13.966396 , 15.309874 ,\n",
       "        16.64747  , 15.420805 , 13.906697 ,  9.679647 ,  7.4885745,\n",
       "         8.588962 ,  8.074124 , 11.057866 , 14.311856 , 13.300421 ,\n",
       "        13.185582 , 11.877043 ],\n",
       "       [12.94247  , 13.02302  ,  8.821633 ,  4.4354076,  7.443942 ,\n",
       "         4.7391725,  3.2644944,  4.5939226,  2.8636043,  8.499213 ,\n",
       "         6.5544214,  5.7511578,  7.6126404,  4.70568  ,  3.2926447,\n",
       "         4.657074 ,  6.4643087,  8.481021 , 11.186175 , 12.678459 ,\n",
       "        12.974821 , 12.003571 ],\n",
       "       [ 5.129241 ,  1.4961169,  1.2153641,  1.1349986,  1.6281488,\n",
       "         1.698103 ,  1.7303177,  1.2103866,  1.3677154,  1.6161397,\n",
       "         1.7180119,  2.1140616,  2.3698606,  1.9535366,  1.987098 ,\n",
       "         2.3518457,  4.2427807,  6.0768895,  7.605426 , 10.348919 ,\n",
       "         9.922935 ,  9.6236925],\n",
       "       [ 1.1369395,  1.046711 ,  1.1083779,  1.2314144,  1.2657518,\n",
       "         1.4740394,  1.1930583,  1.1883323,  1.196894 ,  1.2662768,\n",
       "         1.2528068,  1.604245 ,  1.5676008,  1.5731488,  1.7048157,\n",
       "         2.016459 ,  2.4964812,  3.163672 ,  4.490428 ,  7.559835 ,\n",
       "         6.0730524,  6.1254263]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b48b0e10-a0cb-4288-b05f-39e2a6c50e91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b48b0e10-a0cb-4288-b05f-39e2a6c50e91' class='xr-section-summary' >Coordinates: <span>(2)</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'>float32</div><div class='xr-var-preview xr-preview'>85.25 85.75 86.25 ... 95.25 95.75</div><input id='attrs-e479fd7d-123e-4181-886d-81d5d04448be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e479fd7d-123e-4181-886d-81d5d04448be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05af644d-67f4-4e29-81a1-4c92b35e3c94' class='xr-var-data-in' type='checkbox'><label for='data-05af644d-67f4-4e29-81a1-4c92b35e3c94' 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></dl></div><div class='xr-var-data'><pre>array([85.25, 85.75, 86.25, 86.75, 87.25, 87.75, 88.25, 88.75, 89.25, 89.75,\n",
       "       90.25, 90.75, 91.25, 91.75, 92.25, 92.75, 93.25, 93.75, 94.25, 94.75,\n",
       "       95.25, 95.75], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>18.25 18.75 19.25 ... 28.25 28.75</div><input id='attrs-71502878-63a2-4b7a-a8e2-179066619f48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71502878-63a2-4b7a-a8e2-179066619f48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68a48f56-7fef-4c38-9a76-f4f95697c731' class='xr-var-data-in' type='checkbox'><label for='data-68a48f56-7fef-4c38-9a76-f4f95697c731' 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></dl></div><div class='xr-var-data'><pre>array([18.25, 18.75, 19.25, 19.75, 20.25, 20.75, 21.25, 21.75, 22.25, 22.75,\n",
       "       23.25, 23.75, 24.25, 24.75, 25.25, 25.75, 26.25, 26.75, 27.25, 27.75,\n",
       "       28.25, 28.75], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dae8f9e2-7093-4f98-a8d1-ed21a9e5e9b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dae8f9e2-7093-4f98-a8d1-ed21a9e5e9b4' 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.DataArray 'pre' (lat: 22, lon: 22)>\n",
       "array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan, 34.015903 , 25.502592 ,\n",
       "        18.668839 , 15.103817 ],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 31.91396  , 31.379553 , 23.212328 ,\n",
       "        14.341607 , 12.399574 ],\n",
       "       [ 6.9143767,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan, 28.862612 , 26.161554 , 20.063038 ,\n",
       "        13.50361  , 10.910742 ],\n",
       "       [ 7.677047 ,  8.356039 ,  9.123073 ,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 27.576529 , 25.550064 , 22.138533 , 17.380919 ,\n",
       "        12.470419 ,  9.447939 ],\n",
       "...\n",
       "       [12.969555 , 11.755078 , 10.246152 , 12.192502 , 13.476077 ,\n",
       "        14.6572   , 15.785781 , 16.159964 , 13.966396 , 15.309874 ,\n",
       "        16.64747  , 15.420805 , 13.906697 ,  9.679647 ,  7.4885745,\n",
       "         8.588962 ,  8.074124 , 11.057866 , 14.311856 , 13.300421 ,\n",
       "        13.185582 , 11.877043 ],\n",
       "       [12.94247  , 13.02302  ,  8.821633 ,  4.4354076,  7.443942 ,\n",
       "         4.7391725,  3.2644944,  4.5939226,  2.8636043,  8.499213 ,\n",
       "         6.5544214,  5.7511578,  7.6126404,  4.70568  ,  3.2926447,\n",
       "         4.657074 ,  6.4643087,  8.481021 , 11.186175 , 12.678459 ,\n",
       "        12.974821 , 12.003571 ],\n",
       "       [ 5.129241 ,  1.4961169,  1.2153641,  1.1349986,  1.6281488,\n",
       "         1.698103 ,  1.7303177,  1.2103866,  1.3677154,  1.6161397,\n",
       "         1.7180119,  2.1140616,  2.3698606,  1.9535366,  1.987098 ,\n",
       "         2.3518457,  4.2427807,  6.0768895,  7.605426 , 10.348919 ,\n",
       "         9.922935 ,  9.6236925],\n",
       "       [ 1.1369395,  1.046711 ,  1.1083779,  1.2314144,  1.2657518,\n",
       "         1.4740394,  1.1930583,  1.1883323,  1.196894 ,  1.2662768,\n",
       "         1.2528068,  1.604245 ,  1.5676008,  1.5731488,  1.7048157,\n",
       "         2.016459 ,  2.4964812,  3.163672 ,  4.490428 ,  7.559835 ,\n",
       "         6.0730524,  6.1254263]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 85.25 85.75 86.25 86.75 ... 94.25 94.75 95.25 95.75\n",
       "  * lat      (lat) float32 18.25 18.75 19.25 19.75 ... 27.25 27.75 28.25 28.75"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the data is in mm/month.so we need to devide it by 30.4169 (average of 12 months)\n",
    "\n",
    "pr_mm = jjas_mean/30.4167\n",
    "pr_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4323ee93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting with cartopy jjas_pr_mean\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "plt.rcParams['font.size'] = '16'\n",
    "\n",
    "\n",
    "scale = '110m'\n",
    "\n",
    "fig = plt.figure(figsize=(12,9.5))\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([88,93,20,27])\n",
    "\n",
    "\n",
    " \n",
    "m1 = ax.contourf(pr_mm['lon'][:], pr_mm['lat'][:],pr_mm,\n",
    "                 levels=np.arange(1,35+1,5),transform = ccrs.PlateCarree(),cmap='YlGnBu',extend='both')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Define the xticks for longitude\n",
    "ax.set_xticks(np.arange(87,94,1), crs=ccrs.PlateCarree())\n",
    "lon_formatter = cticker.LongitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "\n",
    "# Define the yticks for latitude\n",
    "ax.set_yticks(np.arange(20,28,1), crs=ccrs.PlateCarree())\n",
    "lat_formatter = cticker.LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.colorbar(m1, orientation = 'horizontal',shrink=0.5, fraction = 0.04 , label = 'rain_rate mm/day')\n",
    "ax.coastlines(linewidth=3)\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=3)\n",
    "\n",
    "#ax.add_feature(cfeature.STATES)\n",
    "#ax.add_feature(cfeature.LAND)\n",
    "#ax.add_feature(cfeature.LAKES)\n",
    "#ax.add_feature(cfeature.OCEAN)\n",
    "#ax.add_feature(cfeature.RIVERS)\n",
    "\n",
    "\n",
    "#ax.add_feature(cfeature.LAND.with_scale(scale))\n",
    "#ax.add_feature(cfeature.OCEAN.with_scale(scale))\n",
    "#ax.add_feature(cfeature.STATES.with_scale(scale))\n",
    "#ax.add_feature(cfeature.LAKES.with_scale(scale), alpha=0.5)\n",
    "#ax.add_feature(cfeature.COASTLINE.with_scale(scale))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#state_borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lakes', scale='50m')#, facecolor='#FFE9B5')\n",
    "#ax.add_feature(state_borders, edgecolor='black')\n",
    "\n",
    " \n",
    "#ax.coastlines(resolution=\"110m\",linewidth=5)\n",
    "#ax.gridlines(linestyle='--',color='black')\n",
    "\n",
    "\n",
    "plt.title('CRU Mean jjas precip rate in mm/day')\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/bd_pr_cru4_jjas_mean.png\")\n",
    "\n",
    "\n",
    "\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pre&#x27; (lat: 360, lon: 720)&gt;\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
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       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b0b063b9-acda-4594-8ac0-cef860946674' class='xr-array-in' type='checkbox' checked><label for='section-b0b063b9-acda-4594-8ac0-cef860946674' 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>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-2922afc4-4309-4bd5-a916-e86fc6d0239a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2922afc4-4309-4bd5-a916-e86fc6d0239a' class='xr-section-summary' >Coordinates: <span>(2)</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'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-54b1db1f-0c8a-45bd-9296-5d5625b69416' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54b1db1f-0c8a-45bd-9296-5d5625b69416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-807ed69f-4bca-4553-976f-f99da90e3e11' class='xr-var-data-in' type='checkbox'><label for='data-807ed69f-4bca-4553-976f-f99da90e3e11' 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></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-eb0dc203-c111-485c-8c4e-a12c44289c92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb0dc203-c111-485c-8c4e-a12c44289c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50163445-1ed1-479b-93be-942123a9762a' class='xr-var-data-in' type='checkbox'><label for='data-50163445-1ed1-479b-93be-942123a9762a' 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></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83324bcf-d77e-47b8-9021-c648513370a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83324bcf-d77e-47b8-9021-c648513370a2' 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>"
      ],
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       "<xarray.DataArray 'pre' (lat: 360, lon: 720)>\n",
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   "source": [
    "annual_mean = data.pre.mean('time')\n",
    "annual_mean"
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  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1a634f5",
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "  --xr-background-color: #111111;\n",
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       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "  margin-top: 0;\n",
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       "\n",
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       "\n",
       ".xr-obj-type {\n",
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       "\n",
       ".xr-sections {\n",
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       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "  display: none;\n",
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       "  padding-left: 0.5em;\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
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       "\n",
       ".xr-section-details {\n",
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       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  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",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\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",
       "}\n",
       "\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",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\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",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
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       "  grid-column: 3;\n",
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       "  color: var(--xr-font-color2);\n",
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       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "  overflow: hidden;\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",
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       "  width: auto;\n",
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       "\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",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
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       "\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",
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       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\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;pre&#x27; (lat: 360, lon: 720)&gt;\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'pre'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-38bb8353-9ce0-4972-a42e-a6f65d747a60' class='xr-array-in' type='checkbox' checked><label for='section-38bb8353-9ce0-4972-a42e-a6f65d747a60' 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>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-85b176ee-fc53-4e9d-a276-ec9fc14119ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85b176ee-fc53-4e9d-a276-ec9fc14119ff' class='xr-section-summary' >Coordinates: <span>(2)</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'>float32</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-e9ad789c-9614-4fac-bd58-ed00d0c61f33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9ad789c-9614-4fac-bd58-ed00d0c61f33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9728cb7a-3fb1-433a-8b18-e10f79d6a823' class='xr-var-data-in' type='checkbox'><label for='data-9728cb7a-3fb1-433a-8b18-e10f79d6a823' 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></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-b3f5fcbd-810c-4c0e-a580-5864f06d3de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3f5fcbd-810c-4c0e-a580-5864f06d3de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6512eb2-04c0-4cd1-abf0-ef409a83fa93' class='xr-var-data-in' type='checkbox'><label for='data-f6512eb2-04c0-4cd1-abf0-ef409a83fa93' 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></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c6a44d5-624d-4078-86bd-ee03bc9abaca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6c6a44d5-624d-4078-86bd-ee03bc9abaca' 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.DataArray 'pre' (lat: 360, lon: 720)>\n",
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
       "  * lat      (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting from mm/month to mm/day\n",
    "\n",
    "annual_pr_mm = annual_mean/30.4167\n",
    "annual_pr_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9d357dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting annual_pr_mean\n",
    "\n",
    "#plotting with cartopy\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,9.5))\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([88,93,20,27])\n",
    "\n",
    "\n",
    "m1 = ax.contourf(annual_pr_mm['lon'], annual_pr_mm['lat'],annual_pr_mm,\n",
    "                 np.arange(1,35+1,5),transform = ccrs.PlateCarree(),cmap='YlGnBu',extend='both')\n",
    "\n",
    " \n",
    "# Define the xticks for longitude\n",
    "ax.set_xticks(np.arange(87,94,1), crs=ccrs.PlateCarree())\n",
    "lon_formatter = cticker.LongitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "\n",
    "# Define the yticks for latitude\n",
    "ax.set_yticks(np.arange(20,28,1), crs=ccrs.PlateCarree())\n",
    "lat_formatter = cticker.LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "plt.colorbar(m1, orientation = 'horizontal',shrink=0.5, fraction = 0.04 , label = 'mm/day')\n",
    "ax.coastlines(linewidth=3)\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=3)\n",
    "\n",
    "\n",
    "plt.title('CRU Annual mean precip rate in mm/day')\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/bd_pr_cru4_annual_mean.png\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "43627803",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mkibria/miniconda3/envs/esp-sp/lib/python3.9/site-packages/xarray/plot/plot.py:1451: FutureWarning: Conversion of the second argument of issubdtype from `str` to `str` is deprecated. In future, it will be treated as `np.str_ == np.dtype(str).type`.\n",
      "  and not np.issubdtype(x.dtype, str)\n",
      "/home/mkibria/miniconda3/envs/esp-sp/lib/python3.9/site-packages/xarray/plot/plot.py:1466: FutureWarning: Conversion of the second argument of issubdtype from `str` to `str` is deprecated. In future, it will be treated as `np.str_ == np.dtype(str).type`.\n",
      "  and not np.issubdtype(y.dtype, str)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f546ebd5f10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data['time.month']\n",
    "\n",
    "y=np.empty_like(annual_mean)\n",
    "k=data['pre.values']\n",
    "z=np.vectorize(y)\n",
    "annual_mean.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c23d7a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f546cd44d30>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr=data.pre\n",
    "pr.sel(lon=90.25).transpose().plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46d3d225",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'var' 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_9420/169088051.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"lon\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"lat\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"PRGn\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"m/s\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'var' is not defined"
     ]
    }
   ],
   "source": [
    "data[var].isel(time=0).plot.contourf(x=\"lon\", y=\"lat\", cmap=\"PRGn\", cbar_kwargs={\"label\": \"m/s\"})\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "385727e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pr.sel(lon=90.25, lat=22.25).plot()  \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5005331",
   "metadata": {},
   "outputs": [],
   "source": [
    "#annual_mean.lon.values\n",
    "\n",
    "#to select the x and y coordinates\n",
    "annual_mean = annual_mean.sel(lon=slice(85,96),lat=slice(18,29))\n",
    "\n",
    "pre=annual_mean.sel(lon=90.25, lat=22.25) \n",
    "\n",
    "pre.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db46a653",
   "metadata": {},
   "outputs": [],
   "source": [
    "month = data['time.month']\n",
    "\n",
    "\n",
    "sst_time_and_zonal_mean = data.pre.mean(dim=('lat', 'lon'))\n",
    "sst_time_and_zonal_mean.plot(x='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf4f2af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_clim = data.pre.groupby('time.month').mean(dim=('lat','lon'))\n",
    "#pre_clim = data.pre.groupby('time.month').mean(dim='time')\n",
    "#pre_clim\n",
    "pre_clim.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1392d35",
   "metadata": {},
   "outputs": [],
   "source": [
    "(pre_clim[6] - pre_clim[0]).plot()\n",
    "plt.title('June minus July precipitation Climatology')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ecbcf40b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pre_clim' 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_5437/219872557.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpre_clim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pre_clim' is not defined"
     ]
    }
   ],
   "source": [
    "pre_clim[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "09f37eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7.4472e+05, 5.8403e+04, 5.1270e+03, 5.2800e+02, 1.5800e+02,\n",
       "        6.0000e+01, 3.0000e+01, 1.0000e+01, 2.0000e+00, 2.0000e+00]),\n",
       " array([   0.     ,  182.54057,  365.08115,  547.6217 ,  730.1623 ,\n",
       "         912.7029 , 1095.2434 , 1277.784  , 1460.3246 , 1642.8652 ,\n",
       "        1825.4058 ], dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre_clim = data.pre.groupby('time.month').mean(dim='time')\n",
    "pre_clim.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b35fb539",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  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",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".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",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\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;month&#x27; (month: 12)&gt;\n",
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'month'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-503593c9-4f86-4679-963f-3ef406d4ccc8' class='xr-array-in' type='checkbox' checked><label for='section-503593c9-4f86-4679-963f-3ef406d4ccc8' 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>1 2 3 4 5 6 7 8 9 10 11 12</span></div><div class='xr-array-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></div></li><li class='xr-section-item'><input id='section-1bd42aff-3e34-45a1-bb47-471690b09837' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1bd42aff-3e34-45a1-bb47-471690b09837' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-3e3b2841-7aa3-48f0-948f-00157315691e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e3b2841-7aa3-48f0-948f-00157315691e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b60b49b-f026-48c2-8b9c-485f0705980e' class='xr-var-data-in' type='checkbox'><label for='data-9b60b49b-f026-48c2-8b9c-485f0705980e' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-166e23bc-4467-4079-86d0-ff975b0146bc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-166e23bc-4467-4079-86d0-ff975b0146bc' 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.DataArray 'month' (month: 12)>\n",
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=pre_clim.month\n",
    "time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76fd7451",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (12,) and (360, 720)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_5437/1255156392.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mannual_mean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/esp-sp/lib/python3.9/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2755\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2756\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2757\u001b[0;31m     return gca().plot(\n\u001b[0m\u001b[1;32m   2758\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2759\u001b[0m         **({\"data\": data} if data is not None else {}), **kwargs)\n",
      "\u001b[0;32m~/miniconda3/envs/esp-sp/lib/python3.9/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1630\u001b[0m         \"\"\"\n\u001b[1;32m   1631\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1632\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1633\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1634\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/esp-sp/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/esp-sp/lib/python3.9/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    499\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    500\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (12,) and (360, 720)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time,annual_mean)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "236a7dc5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f19518",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21b3da30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}