{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "280aa2ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import salem\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a5dea1",
   "metadata": {},
   "source": [
    "## Read in raw 2m temperature data from tigress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "03e1c791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to min temp data\n",
    "p1 = '/tigress/wenchang/data/era5/raw/2m_temperature_min'\n",
    "# path to avg temp data\n",
    "p2 = '/tigress/wenchang/data/era5/raw/2m_temperature'\n",
    "# path to max temp data\n",
    "p3 = '/tigress/wenchang/data/era5/raw/2m_temperature_max'\n",
    "\n",
    "paths = {p1, p2, p3}\n",
    "\n",
    "for path in paths:\n",
    "    \n",
    "    # for labeling dataset\n",
    "    if 'min' in path:\n",
    "        v = 'dmn'\n",
    "    elif 'max' in path:\n",
    "        v = 'dmx'\n",
    "    else:\n",
    "        v = 'da'\n",
    "    \n",
    "    # retrieve all files in dataset\n",
    "    files = []\n",
    "    for file in os.listdir(path):\n",
    "        if file.endswith(\".nc\"):\n",
    "            files.append(os.path.join(path, file))\n",
    "    # store dataset\n",
    "    globals()[v] = xr.open_mfdataset(files)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19257cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2m temp min: \n",
      " <xarray.Dataset>\n",
      "Dimensions:    (longitude: 1440, latitude: 721, time: 360144)\n",
      "Coordinates:\n",
      "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
      "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
      "  * time       (time) datetime64[ns] 1979-01-01 ... 2020-01-31T23:00:00\n",
      "Data variables:\n",
      "    mn2t       (time, latitude, longitude) float32 dask.array<chunksize=(744, 721, 1440), meta=np.ndarray>\n",
      "Attributes:\n",
      "    Conventions:  CF-1.6\n",
      "    history:      2020-07-28 05:27:28 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n",
      "\n",
      " 2m temp avg: \n",
      " <xarray.Dataset>\n",
      "Dimensions:    (longitude: 1440, latitude: 721, time: 372528)\n",
      "Coordinates:\n",
      "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
      "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
      "  * time       (time) datetime64[ns] 1979-01-01 ... 2021-06-30T23:00:00\n",
      "Data variables:\n",
      "    t2m        (time, latitude, longitude) float32 dask.array<chunksize=(744, 721, 1440), meta=np.ndarray>\n",
      "Attributes:\n",
      "    Conventions:  CF-1.6\n",
      "    history:      2019-04-29 21:28:40 GMT by grib_to_netcdf-2.10.0: /opt/ecmw...\n",
      "\n",
      " 2m temp max: \n",
      " <xarray.Dataset>\n",
      "Dimensions:    (longitude: 1440, latitude: 721, time: 360144)\n",
      "Coordinates:\n",
      "  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
      "  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
      "  * time       (time) datetime64[ns] 1979-01-01 ... 2020-01-31T23:00:00\n",
      "Data variables:\n",
      "    mx2t       (time, latitude, longitude) float32 dask.array<chunksize=(744, 721, 1440), meta=np.ndarray>\n",
      "Attributes:\n",
      "    Conventions:  CF-1.6\n",
      "    history:      2020-07-28 05:07:56 GMT by grib_to_netcdf-2.16.0: /opt/ecmw...\n"
     ]
    }
   ],
   "source": [
    "print('2m temp min: \\n', dmn)\n",
    "\n",
    "print('\\n 2m temp avg: \\n', da)\n",
    "\n",
    "print('\\n 2m temp max: \\n', dmx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b437e367",
   "metadata": {},
   "source": [
    "## Extract 2m temperature data in some rectangular region "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d3ae0401",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lat/lon boundaries\n",
    "lon1 = 69.5\n",
    "lon2 = 70\n",
    "lat1 = 34\n",
    "lat2 = 33.5\n",
    "\n",
    "# time frame\n",
    "t1 = '1979'\n",
    "t2 = '1980'\n",
    "\n",
    "# Dataset within given lat/lon region, and time frame\n",
    "dmnb = dmn.sel(longitude=slice(lon1, lon2), latitude=slice(lat1, lat2), time=slice(t1, t2))\n",
    "dmxb = dmx.sel(longitude=slice(lon1, lon2), latitude=slice(lat1, lat2), time=slice(t1, t2))\n",
    "dab = da.sel(longitude=slice(lon1, lon2), latitude=slice(lat1, lat2), time=slice(t1, t2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "eeba9aae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract arrays for min temp, max temp, avg temp over all times\n",
    "mn2t = dmnb.mn2t\n",
    "mx2t = dmxb.mx2t\n",
    "a2t = dab.t2m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c96278a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_2t = a2t.isel(time=1).plot(levels=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d89a86a",
   "metadata": {},
   "source": [
    "## Establish a monthly climatology in the region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f48b6f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# monthly average of average daily temperature\n",
    "a2t_clim_a = a2t.groupby('time.month').mean('time')\n",
    "a2t_clim_s = a2t.groupby('time.month').std('time')\n",
    "\n",
    "# monthly average of maximum daily temperature\n",
    "mx2t_clim = mx2t.groupby('time.month').mean('time')\n",
    "\n",
    "# monthly average of minimum daily temperature\n",
    "mn2t_clim = mn2t.groupby('time.month').mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8077c639",
   "metadata": {},
   "source": [
    "Average 2m temperature, monthly average:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0ee3c696",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x2abdcf7b0f40>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x432 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a2t_clim_a.plot.contourf(col='month', col_wrap=6, levels=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302b497a",
   "metadata": {},
   "source": [
    "Average 2m temperature, monthly standard deviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "bdb212a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x2abdcf558730>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x432 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a2t_clim_s.plot.contourf(col='month', col_wrap=6, levels=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d667bcd4",
   "metadata": {},
   "source": [
    "## Define a heatwave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "fcde0fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(269.92688, dtype=float32)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2t_clim_a.isel(month=1,latitude=1,longitude=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7a353ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(263.73718, dtype=float32)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2t.isel(time=1,latitude=1,longitude=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "ce64955d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate monthly temperature anomaly\n",
    "anom = a2t.groupby('time.month') - a2t_clim_a\n",
    "anom = anom.groupby('time.month') - a2t_clim_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "20d78b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "anom = anom.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "56632f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtr_h = anom.where(anom.t2m>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "fd99fac6",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'isel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-183-7c9dcf7d7d1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxtr_h\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;36m100\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[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5463\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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   5464\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5465\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5467\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'isel'"
     ]
    }
   ],
   "source": [
    "xtr_h.isel(time=100).plot.contourf(levels=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "e2ed58be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.indexing._LocIndexer at 0x2abc4e294f90>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtr_h.loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "4ec7510b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t2m&#x27; (time: 15605, latitude: 3, longitude: 3)&gt;\n",
       "dask.array&lt;where, shape=(15605, 3, 3), dtype=float32, chunksize=(742, 3, 3), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
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       "  * latitude   (latitude) float32 34.0 33.75 33.5\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 1980-12-31T23:00:00\n",
       "    month      (time) int64 1 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t2m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 15605</li><li><span class='xr-has-index'>latitude</span>: 3</li><li><span class='xr-has-index'>longitude</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-43fbf431-3a33-44b4-a4fd-8706cfacb562' class='xr-array-in' type='checkbox' checked><label for='section-43fbf431-3a33-44b4-a4fd-8706cfacb562' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(601, 3, 3), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">15605</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-3b979b8e-6c8f-4e04-bb70-12b5e892610d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b979b8e-6c8f-4e04-bb70-12b5e892610d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>69.5 69.75 70.0</div><input id='attrs-ee5a7f94-407a-4625-9d66-8610e37c8cb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee5a7f94-407a-4625-9d66-8610e37c8cb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67d0948b-f910-4acf-b9c2-4de1bd32c28b' class='xr-var-data-in' type='checkbox'><label for='data-67d0948b-f910-4acf-b9c2-4de1bd32c28b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([69.5 , 69.75, 70.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>34.0 33.75 33.5</div><input id='attrs-118e6a6c-38b4-4090-8cca-49a894c27eed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-118e6a6c-38b4-4090-8cca-49a894c27eed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb5102a7-7531-467c-8011-9666b347c5d4' class='xr-var-data-in' type='checkbox'><label for='data-bb5102a7-7531-467c-8011-9666b347c5d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([34.  , 33.75, 33.5 ], 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'>1979-01-01 ... 1980-12-31T23:00:00</div><input id='attrs-9eb962f4-09c8-4f82-bacb-033fc8580782' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eb962f4-09c8-4f82-bacb-033fc8580782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f65e3632-6c96-4698-81f5-df712c2bbfa2' class='xr-var-data-in' type='checkbox'><label for='data-f65e3632-6c96-4698-81f5-df712c2bbfa2' 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;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T02:00:00.000000000&#x27;, ..., &#x27;1980-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-31T22:00:00.000000000&#x27;, &#x27;1980-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 ... 12 12 12 12 12 12</div><input id='attrs-6457dd11-ef0c-42b4-b92f-1e444ee55521' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6457dd11-ef0c-42b4-b92f-1e444ee55521' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc8fd177-501f-434c-95d0-ea75568238c3' class='xr-var-data-in' type='checkbox'><label for='data-dc8fd177-501f-434c-95d0-ea75568238c3' 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,  1,  1, ..., 12, 12, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ea553b8-89a5-408e-adce-8d1263b2926e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0ea553b8-89a5-408e-adce-8d1263b2926e' 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 't2m' (time: 15605, latitude: 3, longitude: 3)>\n",
       "dask.array<where, shape=(15605, 3, 3), dtype=float32, chunksize=(742, 3, 3), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 69.5 69.75 70.0\n",
       "  * latitude   (latitude) float32 34.0 33.75 33.5\n",
       "  * time       (time) datetime64[ns] 1979-01-01 ... 1980-12-31T23:00:00\n",
       "    month      (time) int64 1 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "anom.where(anom<0, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "2bdc6195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xarray.core.dataarray.DataArray"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(anom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9563c072",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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