Xarray_and_simple_plotting-Output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Aim:` *Notebook for the purpose of Climate Risk Assessment lecture:* `Xarray and Simple Plotting`\n",
    "\n",
    "`Author:` Mubashshir Ali"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:54:36.300750Z",
     "start_time": "2020-02-17T14:54:35.523804Z"
    }
   },
   "outputs": [],
   "source": [
    "# importing libraries as an alias so that we know which function belongs to which library\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting a simple canvas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first plot a simple canvas on which we will draw figures later-on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:54:47.344926Z",
     "start_time": "2020-02-17T14:54:46.801011Z"
    }
   },
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `shift` + `tab` key to view the syntax of a function and other options it provides. Test it below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:54:49.218054Z",
     "start_time": "2020-02-17T14:54:49.204533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot a simple function, the classical `sine` curve example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:54:50.952724Z",
     "start_time": "2020-02-17T14:54:50.679513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f60a9907c50>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# create an array of points\n",
    "x = np.linspace(0, 10, 1000) # use shift + tab to see the syntax\n",
    "y = np.sin(x) # our function\n",
    "\n",
    "# lets create our canvas as we did above\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This could also be plotted lazily in just one line..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:54:57.532616Z",
     "start_time": "2020-02-17T14:54:57.313124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f60a9878c10>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(x, np.sin(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making nicer plots with seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:55:07.075332Z",
     "start_time": "2020-02-17T14:55:05.622350Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='whitegrid') # sets default plotting styles, check contextual help for more options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-05T15:48:23.640739Z",
     "start_time": "2020-02-05T15:48:23.636167Z"
    }
   },
   "outputs": [],
   "source": [
    "# setting color palette\n",
    "#sns.set_palette(palette='tab20')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Same plot as above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:55:12.682356Z",
     "start_time": "2020-02-17T14:55:12.447822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5fbbf7ae10>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploting multiple lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:55:19.771619Z",
     "start_time": "2020-02-17T14:55:19.430797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5fbbeeea50>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets do the lazy ploting of multiple lines\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that matplotlib automatically assigned different colours. Pretty smart, right? What if you don't like those colours? Let's see how we can customize it..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting colors and linestyles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:55:21.956319Z",
     "start_time": "2020-02-17T14:55:21.670091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x - 0), color='blue', linestyle='-')        # specify color by name\n",
    "plt.plot(x, np.sin(x - 1), color='g', linestyle='--')           # short color code (rgbcmyk)\n",
    "plt.plot(x, np.sin(x - 2), color='0.75', linestyle='-.')        # Grayscale between 0 and 1\n",
    "plt.plot(x, np.sin(x - 3), color='#FFDD44', linestyle=':')     # Hex code (RRGGBB from 00 to FF)\n",
    "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 and 1\n",
    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Find a website with a list of HTML color names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you know by now the climate model data is stored in `NetCDF` format which is a special way of storing multi-dimensional data. We will use a python library called `xarray` which is specifically designed to make our life easier to handle multi-dimensional data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets import the libary as an alias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:58:47.542513Z",
     "start_time": "2020-02-17T14:58:47.106263Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "import xarray as xr\n",
    "# library to work with climate model data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T14:58:50.084785Z",
     "start_time": "2020-02-17T14:58:50.074595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://xarray.pydata.org/en/stable/_images/dataset-diagram.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='http://xarray.pydata.org/en/stable/_images/dataset-diagram.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how a multi-dimensional dataset looks like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xarray Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T15:02:37.864711Z",
     "start_time": "2020-02-17T15:02:37.857912Z"
    }
   },
   "outputs": [],
   "source": [
    "# file to be plotted\n",
    "# Adapt it as per your need\n",
    "\n",
    "file1 = '/climriskdata/EUR-11N/ICHEC-EC-EARTH_SMHI-RCA4_v1/rcp85/tas/reduced_tas_EUR-11_ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4_v1_day_20710101-20751231_LL.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-17T15:02:38.961080Z",
     "start_time": "2020-02-17T15:02:38.078586Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  --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",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\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, 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:    (bnds: 2, lat: 41, lon: 61, time: 1826)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2071-01-01T12:00:00 ... 2075-12-31T12:00:00\n",
       "  * lon        (lon) float64 5.0 5.1 5.2 5.3 5.4 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat        (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.7 47.8 47.9 48.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] 2071-01-01 2071-01-02 ... 2076-01-01\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:                            Climate Data Interface version ?? (http:/...\n",
       "    history:                        Fri Mar 13 11:12:41 2020: cdo sellonlatbo...\n",
       "    institution:                    Swedish Meteorological and Hydrological I...\n",
       "    Conventions:                    CF-1.4\n",
       "    contact:                        rossby.cordex@smhi.se\n",
       "    creation_date:                  2013-07-03-T20:13:36Z\n",
       "    experiment:                     RCP8.5\n",
       "    experiment_id:                  rcp85\n",
       "    driving_experiment:             ICHEC-EC-EARTH, rcp85, r12i1p1\n",
       "    driving_model_id:               ICHEC-EC-EARTH\n",
       "    driving_model_ensemble_member:  r12i1p1\n",
       "    driving_experiment_name:        rcp85\n",
       "    frequency:                      day\n",
       "    institute_id:                   SMHI\n",
       "    model_id:                       SMHI-RCA4\n",
       "    rcm_version_id:                 v1\n",
       "    project_id:                     CORDEX\n",
       "    CORDEX_domain:                  EUR-11\n",
       "    product:                        output\n",
       "    references:                     http://www.smhi.se/en/Research/Research-d...\n",
       "    tracking_id:                    18802e76-c2be-49a8-bbe6-c460dcd5960a\n",
       "    rossby_comment:                 201139: CORDEX Europe 0.11 deg | RCA4 v1 ...\n",
       "    rossby_run_id:                  201139\n",
       "    rossby_grib_path:               /nobackup/rossby16/rossby/joint_exp/corde...\n",
       "    CDO:                            Climate Data Operators version 1.9.3 (htt...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-736cdc56-0943-4600-b481-3a7bb7159c67' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-736cdc56-0943-4600-b481-3a7bb7159c67' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 61</li><li><span class='xr-has-index'>time</span>: 1826</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-175971f1-7d32-4158-b26e-b7e62bc9621c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-175971f1-7d32-4158-b26e-b7e62bc9621c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-01-01T12:00:00 ... 2075-12-...</div><input id='attrs-3294bf99-e553-4240-b6a6-7e9fb227bf0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3294bf99-e553-4240-b6a6-7e9fb227bf0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1969c74-593c-47a8-9344-f042223ce12e' class='xr-var-data-in' type='checkbox'><label for='data-d1969c74-593c-47a8-9344-f042223ce12e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2071-01-01T12:00:00.000000000&#x27;, &#x27;2071-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2071-01-03T12:00:00.000000000&#x27;, ..., &#x27;2075-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2075-12-30T12:00:00.000000000&#x27;, &#x27;2075-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.1 5.2 5.3 ... 10.8 10.9 11.0</div><input id='attrs-93a36b53-8ceb-493f-a691-98b17d52942b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93a36b53-8ceb-493f-a691-98b17d52942b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e7e222e-db7f-4a84-9bad-04d70fbdf585' class='xr-var-data-in' type='checkbox'><label for='data-7e7e222e-db7f-4a84-9bad-04d70fbdf585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,\n",
       "        6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,\n",
       "        7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,\n",
       "        8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.1 44.2 ... 47.8 47.9 48.0</div><input id='attrs-babf6285-7b2a-4d9d-89c4-b19a3f9e5e04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-babf6285-7b2a-4d9d-89c4-b19a3f9e5e04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f96d89e-13e2-45e6-805a-2725f5f146de' class='xr-var-data-in' type='checkbox'><label for='data-4f96d89e-13e2-45e6-805a-2725f5f146de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,\n",
       "       45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3,\n",
       "       46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5,\n",
       "       47.6, 47.7, 47.8, 47.9, 48. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f4abe29-69df-4f44-acdb-3ef0c4791d10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f4abe29-69df-4f44-acdb-3ef0c4791d10' 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>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e240e66-877a-4902-8f7a-0346d14ab73f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e240e66-877a-4902-8f7a-0346d14ab73f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4ff31a0-6f0f-478b-a782-8ecd6c2fe057' class='xr-var-data-in' type='checkbox'><label for='data-a4ff31a0-6f0f-478b-a782-8ecd6c2fe057' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2071-01-01T00:00:00.000000000&#x27;, &#x27;2071-01-02T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2071-01-02T00:00:00.000000000&#x27;, &#x27;2071-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2071-01-03T00:00:00.000000000&#x27;, &#x27;2071-01-04T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2075-12-29T00:00:00.000000000&#x27;, &#x27;2075-12-30T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2075-12-30T00:00:00.000000000&#x27;, &#x27;2075-12-31T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2075-12-31T00:00:00.000000000&#x27;, &#x27;2076-01-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-badb2984-e3af-43a1-a874-85ca52f0c902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-badb2984-e3af-43a1-a874-85ca52f0c902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae800ea2-1735-4b7c-a5c9-7a4a91e96381' class='xr-var-data-in' type='checkbox'><label for='data-ae800ea2-1735-4b7c-a5c9-7a4a91e96381' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[4566826 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c7e1183-85da-49f4-8e20-84f8e3f03870' class='xr-section-summary-in' type='checkbox'  ><label for='section-7c7e1183-85da-49f4-8e20-84f8e3f03870' class='xr-section-summary' >Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version ?? (http://mpimet.mpg.de/cdi)</dd><dt><span>history :</span></dt><dd>Fri Mar 13 11:12:41 2020: cdo sellonlatbox,5,11,44,48 /tank23/exports/CRA_10yrs/10yrs/ICHEC-EC-EARTH_SMHI-RCA4_v1/rcp85/tas/tas_EUR-11_ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4_v1_day_20710101-20751231_LL.nc /tank23/exports/CRA_10yrs/EUR-11N/ICHEC-EC-EARTH_SMHI-RCA4_v1/rcp85/tas/reduced_tas_EUR-11_ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4_v1_day_20710101-20751231_LL.nc\n",
       "Sat Jan 12 10:23:17 2019: cdo remapbil,llgriddesSCA.txt .././EUR-11/ICHEC-EC-EARTH_SMHI-RCA4_v1/rcp85/tas/tas_EUR-11_ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4_v1_day_20710101-20751231.nc ./EUR-11/ICHEC-EC-EARTH_SMHI-RCA4_v1/rcp85/tas/tas_EUR-11_ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4_v1_day_20710101-20751231_LL.nc</dd><dt><span>institution :</span></dt><dd>Swedish Meteorological and Hydrological Institute, Rossby Centre</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>contact :</span></dt><dd>rossby.cordex@smhi.se</dd><dt><span>creation_date :</span></dt><dd>2013-07-03-T20:13:36Z</dd><dt><span>experiment :</span></dt><dd>RCP8.5</dd><dt><span>experiment_id :</span></dt><dd>rcp85</dd><dt><span>driving_experiment :</span></dt><dd>ICHEC-EC-EARTH, rcp85, r12i1p1</dd><dt><span>driving_model_id :</span></dt><dd>ICHEC-EC-EARTH</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r12i1p1</dd><dt><span>driving_experiment_name :</span></dt><dd>rcp85</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>institute_id :</span></dt><dd>SMHI</dd><dt><span>model_id :</span></dt><dd>SMHI-RCA4</dd><dt><span>rcm_version_id :</span></dt><dd>v1</dd><dt><span>project_id :</span></dt><dd>CORDEX</dd><dt><span>CORDEX_domain :</span></dt><dd>EUR-11</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>references :</span></dt><dd>http://www.smhi.se/en/Research/Research-departments/climate-research-rossby-centre</dd><dt><span>tracking_id :</span></dt><dd>18802e76-c2be-49a8-bbe6-c460dcd5960a</dd><dt><span>rossby_comment :</span></dt><dd>201139: CORDEX Europe 0.11 deg | RCA4 v1 | EC-EARTH | r12i1p1 | rcp85 | L40</dd><dt><span>rossby_run_id :</span></dt><dd>201139</dd><dt><span>rossby_grib_path :</span></dt><dd>/nobackup/rossby16/rossby/joint_exp/cordex/201139/raw/</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.3 (http://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 41, lon: 61, time: 1826)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2071-01-01T12:00:00 ... 2075-12-31T12:00:00\n",
       "  * lon        (lon) float64 5.0 5.1 5.2 5.3 5.4 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat        (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.7 47.8 47.9 48.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] ...\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:                            Climate Data Interface version ?? (http:/...\n",
       "    history:                        Fri Mar 13 11:12:41 2020: cdo sellonlatbo...\n",
       "    institution:                    Swedish Meteorological and Hydrological I...\n",
       "    Conventions:                    CF-1.4\n",
       "    contact:                        rossby.cordex@smhi.se\n",
       "    creation_date:                  2013-07-03-T20:13:36Z\n",
       "    experiment:                     RCP8.5\n",
       "    experiment_id:                  rcp85\n",
       "    driving_experiment:             ICHEC-EC-EARTH, rcp85, r12i1p1\n",
       "    driving_model_id:               ICHEC-EC-EARTH\n",
       "    driving_model_ensemble_member:  r12i1p1\n",
       "    driving_experiment_name:        rcp85\n",
       "    frequency:                      day\n",
       "    institute_id:                   SMHI\n",
       "    model_id:                       SMHI-RCA4\n",
       "    rcm_version_id:                 v1\n",
       "    project_id:                     CORDEX\n",
       "    CORDEX_domain:                  EUR-11\n",
       "    product:                        output\n",
       "    references:                     http://www.smhi.se/en/Research/Research-d...\n",
       "    tracking_id:                    18802e76-c2be-49a8-bbe6-c460dcd5960a\n",
       "    rossby_comment:                 201139: CORDEX Europe 0.11 deg | RCA4 v1 ...\n",
       "    rossby_run_id:                  201139\n",
       "    rossby_grib_path:               /nobackup/rossby16/rossby/joint_exp/corde...\n",
       "    CDO:                            Climate Data Operators version 1.9.3 (htt..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets load the data by using open_dataset function of xarray\n",
    "\n",
    "ds1 = xr.open_dataset(file1)\n",
    "ds1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------\n",
    "Here, you can see one of the reasons why `NetCDF` format is used. It presevers the metadata like standard name, units etc.\n",
    "\n",
    "What are the dimensions of the tas variable? \n",
    "\n",
    "`Note:` If your data file has only one variable then one can also use `xr.open_datarray()` to open the file "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataArray/Data variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Array is the actual data which the dataset holds. It can be a N-D numpy array. A dataset can hold multiple data variables. \n",
    "\n",
    "We see that this dataset has variables called `time_bnds` and `tas`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every `DataArray` contains:\n",
    "    \n",
    "* `dimensions`: Eg, lat, lon, time\n",
    "* `coordinates`: labels of each point in the form of a Python-dictionary\n",
    "* `attributes`: Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference between dimensions and coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coordinates are labels to your dimensions. This will be more clear when we look at selecting data examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataArray properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tas'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.name"
   ]
  },
  {
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   "execution_count": 9,
   "metadata": {
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    "jupyter": {
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    {
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      "text/plain": [
       "array([[[282.3606 , 281.92233, 281.38867, ..., 281.33218, 281.44113,\n",
       "         281.81854],\n",
       "        [282.17743, 281.61105, 280.8188 , ..., 280.02493, 280.3555 ,\n",
       "         280.87204],\n",
       "        [281.76346, 281.17737, 280.14703, ..., 280.2751 , 280.7882 ,\n",
       "         281.293  ],\n",
       "        ...,\n",
       "        [279.64423, 279.542  , 279.47067, ..., 279.77545, 279.8155 ,\n",
       "         279.86526],\n",
       "        [279.83334, 279.6843 , 279.56104, ..., 279.0776 , 279.01532,\n",
       "         278.93643],\n",
       "        [279.9746 , 279.82288, 279.6683 , ..., 278.4002 , 278.3426 ,\n",
       "         278.20987]],\n",
       "\n",
       "       [[281.08258, 280.2242 , 279.31454, ..., 279.23026, 279.34784,\n",
       "         279.6531 ],\n",
       "        [281.1279 , 280.26575, 279.3664 , ..., 277.66495, 278.0906 ,\n",
       "         278.72525],\n",
       "        [281.11157, 280.50165, 279.71872, ..., 278.0074 , 278.70734,\n",
       "         279.40582],\n",
       "        ...,\n",
       "        [283.28638, 283.16043, 283.0469 , ..., 280.37994, 280.44113,\n",
       "         280.511  ],\n",
       "        [283.43243, 283.2795 , 283.1321 , ..., 280.33997, 280.32675,\n",
       "         280.3005 ],\n",
       "        [283.57343, 283.40277, 283.22156, ..., 280.3977 , 280.33777,\n",
       "         280.24258]],\n",
       "\n",
       "       [[283.10718, 281.60428, 280.0746 , ..., 282.55768, 282.69427,\n",
       "         282.91922],\n",
       "        [283.13397, 281.4543 , 280.03833, ..., 281.47052, 281.90076,\n",
       "         282.4488 ],\n",
       "        [282.78183, 281.32187, 280.10446, ..., 281.86533, 282.4782 ,\n",
       "         283.03683],\n",
       "        ...,\n",
       "        [279.7016 , 279.67606, 279.6942 , ..., 279.4371 , 279.71335,\n",
       "         280.0081 ],\n",
       "        [279.6872 , 279.57492, 279.51413, ..., 279.69443, 279.89334,\n",
       "         280.12402],\n",
       "        [279.73184, 279.579  , 279.4567 , ..., 279.7435 , 279.81427,\n",
       "         279.92523]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[285.66354, 284.8385 , 283.77286, ..., 277.2443 , 276.86548,\n",
       "         276.7792 ],\n",
       "        [285.35947, 284.44333, 283.2433 , ..., 277.74097, 278.14056,\n",
       "         278.42664],\n",
       "        [284.84467, 284.0199 , 282.79312, ..., 278.44788, 279.0367 ,\n",
       "         279.37238],\n",
       "        ...,\n",
       "        [281.64575, 281.54312, 281.4906 , ..., 275.94434, 276.32468,\n",
       "         276.63022],\n",
       "        [281.87784, 281.74036, 281.61658, ..., 276.69827, 276.95682,\n",
       "         277.10095],\n",
       "        [281.98077, 281.85876, 281.68875, ..., 277.00317, 277.1255 ,\n",
       "         277.09177]],\n",
       "\n",
       "       [[288.16977, 287.3145 , 286.17545, ..., 280.62122, 280.48083,\n",
       "         280.76968],\n",
       "        [287.98773, 286.96713, 285.6212 , ..., 280.18433, 280.62778,\n",
       "         281.10486],\n",
       "        [287.47653, 286.5798 , 285.19022, ..., 280.55856, 281.19046,\n",
       "         281.73938],\n",
       "        ...,\n",
       "        [283.69385, 283.58005, 283.53458, ..., 279.51862, 279.75803,\n",
       "         280.0345 ],\n",
       "        [283.99002, 283.8089 , 283.68237, ..., 279.8073 , 279.654  ,\n",
       "         279.50485],\n",
       "        [284.22964, 284.0202 , 283.82617, ..., 279.8305 , 279.38168,\n",
       "         278.87146]],\n",
       "\n",
       "       [[288.25076, 287.38074, 286.26273, ..., 281.76877, 281.71143,\n",
       "         281.9936 ],\n",
       "        [287.97815, 287.0298 , 285.77942, ..., 281.39896, 281.96222,\n",
       "         282.53558],\n",
       "        [287.47018, 286.6483 , 285.34122, ..., 282.02368, 282.83435,\n",
       "         283.5832 ],\n",
       "        ...,\n",
       "        [284.02374, 283.98428, 283.9885 , ..., 283.74054, 283.8244 ,\n",
       "         283.9584 ],\n",
       "        [284.14594, 284.06918, 284.04956, ..., 283.90512, 283.6821 ,\n",
       "         283.43613],\n",
       "        [284.2102 , 284.14322, 284.09113, ..., 284.07794, 283.62598,\n",
       "         283.03836]]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('time', 'lat', 'lon')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2071-01-01T12:00:00 ... 2075-12-31T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'standard_name': 'air_temperature',\n",
       " 'long_name': 'Near-Surface Air Temperature',\n",
       " 'units': 'K',\n",
       " 'comment': 'daily-mean near-surface (usually, 2 meter) air temperature.',\n",
       " 'cell_methods': 'time: mean',\n",
       " 'history': \"2016-02-01T11:13:29Z altered by CMOR: Treated scalar dimension: 'height'.\",\n",
       " 'associated_files': 'gridspecFile: gridspec_atmos_fx_MPI-CSC-REMO2009_historical_r0i0p0.nc'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.attrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different Ways of Selecting Data\n",
    "\n",
    "Basic positional based indexing same as in numpy works, but one has to know the specific position to extract the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; ()&gt;\n",
       "array(281.61105, dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-01-01T12:00:00\n",
       "    lon      float64 5.1\n",
       "    lat      float64 44.1\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-de54bc84-ab2f-407b-80ad-aacb363dc17c' class='xr-array-in' type='checkbox' checked><label for='section-de54bc84-ab2f-407b-80ad-aacb363dc17c' 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>281.61105</span></div><div class='xr-array-data'><pre>array(281.61105, dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-5c6d4110-9442-4494-b712-a097325c5a0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c6d4110-9442-4494-b712-a097325c5a0f' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-01-01T12:00:00</div><input id='attrs-f0aa02a5-7599-4273-a51c-9cbd3ab72501' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0aa02a5-7599-4273-a51c-9cbd3ab72501' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1581251-ef8b-4577-87b9-8f96cc9d2bb2' class='xr-var-data-in' type='checkbox'><label for='data-c1581251-ef8b-4577-87b9-8f96cc9d2bb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2071-01-01T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.1</div><input id='attrs-d0743666-3ae2-4aa5-a50c-bf9c2ae6f8f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0743666-3ae2-4aa5-a50c-bf9c2ae6f8f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ed93415-22ab-49ae-94ac-16a7479f213a' class='xr-var-data-in' type='checkbox'><label for='data-4ed93415-22ab-49ae-94ac-16a7479f213a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array(5.1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.1</div><input id='attrs-77bae802-ee32-4671-9143-aaa635074878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77bae802-ee32-4671-9143-aaa635074878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d6d53bd-4049-4eb5-9e6d-41c0b6375eac' class='xr-var-data-in' type='checkbox'><label for='data-6d6d53bd-4049-4eb5-9e6d-41c0b6375eac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array(44.1)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b38c723-951e-4e51-b735-d3b6263553e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b38c723-951e-4e51-b735-d3b6263553e8' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' ()>\n",
       "array(281.61105, dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-01-01T12:00:00\n",
       "    lon      float64 5.1\n",
       "    lat      float64 44.1\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas[0,1,1] # gives the time=0, lat=1, lon=1 position respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:57:56.058360Z",
     "start_time": "2019-06-21T13:57:56.023242Z"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; (lat: 41, lon: 61)&gt;\n",
       "array([[282.3606 , 281.92233, 281.38867, ..., 281.33218, 281.44113, 281.81854],\n",
       "       [282.17743, 281.61105, 280.8188 , ..., 280.02493, 280.3555 , 280.87204],\n",
       "       [281.76346, 281.17737, 280.14703, ..., 280.2751 , 280.7882 , 281.293  ],\n",
       "       ...,\n",
       "       [279.64423, 279.542  , 279.47067, ..., 279.77545, 279.8155 , 279.86526],\n",
       "       [279.83334, 279.6843 , 279.56104, ..., 279.0776 , 279.01532, 278.93643],\n",
       "       [279.9746 , 279.82288, 279.6683 , ..., 278.4002 , 278.3426 , 278.20987]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-01-01T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d4b6e7ef-3c63-402d-9dd3-29526fa7f953' class='xr-array-in' type='checkbox' checked><label for='section-d4b6e7ef-3c63-402d-9dd3-29526fa7f953' 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>282.3606 281.92233 281.38867 280.83374 ... 278.4002 278.3426 278.20987</span></div><div class='xr-array-data'><pre>array([[282.3606 , 281.92233, 281.38867, ..., 281.33218, 281.44113, 281.81854],\n",
       "       [282.17743, 281.61105, 280.8188 , ..., 280.02493, 280.3555 , 280.87204],\n",
       "       [281.76346, 281.17737, 280.14703, ..., 280.2751 , 280.7882 , 281.293  ],\n",
       "       ...,\n",
       "       [279.64423, 279.542  , 279.47067, ..., 279.77545, 279.8155 , 279.86526],\n",
       "       [279.83334, 279.6843 , 279.56104, ..., 279.0776 , 279.01532, 278.93643],\n",
       "       [279.9746 , 279.82288, 279.6683 , ..., 278.4002 , 278.3426 , 278.20987]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-7448ef26-a83b-4bd0-8226-4bbc05e203eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7448ef26-a83b-4bd0-8226-4bbc05e203eb' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-01-01T12:00:00</div><input id='attrs-b0cd60f3-a0d7-4119-9860-c5c31585aae5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0cd60f3-a0d7-4119-9860-c5c31585aae5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33288ea7-007f-4f59-b027-08cd78f095f8' class='xr-var-data-in' type='checkbox'><label for='data-33288ea7-007f-4f59-b027-08cd78f095f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2071-01-01T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.1 5.2 5.3 ... 10.8 10.9 11.0</div><input id='attrs-5cd11c82-55ae-4b42-9bc6-0d95944a9312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cd11c82-55ae-4b42-9bc6-0d95944a9312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6fa3a8f-b295-4b0b-8ca7-e8d941bc95de' class='xr-var-data-in' type='checkbox'><label for='data-c6fa3a8f-b295-4b0b-8ca7-e8d941bc95de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,\n",
       "        6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,\n",
       "        7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,\n",
       "        8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.1 44.2 ... 47.8 47.9 48.0</div><input id='attrs-3a856297-da21-4d1f-b48a-2d81a95b267a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a856297-da21-4d1f-b48a-2d81a95b267a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3a3a114-7476-45fb-853c-e28bbce742c5' class='xr-var-data-in' type='checkbox'><label for='data-e3a3a114-7476-45fb-853c-e28bbce742c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,\n",
       "       45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3,\n",
       "       46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5,\n",
       "       47.6, 47.7, 47.8, 47.9, 48. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d54084d-b3aa-4571-b372-f42970574980' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d54084d-b3aa-4571-b372-f42970574980' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (lat: 41, lon: 61)>\n",
       "array([[282.3606 , 281.92233, 281.38867, ..., 281.33218, 281.44113, 281.81854],\n",
       "       [282.17743, 281.61105, 280.8188 , ..., 280.02493, 280.3555 , 280.87204],\n",
       "       [281.76346, 281.17737, 280.14703, ..., 280.2751 , 280.7882 , 281.293  ],\n",
       "       ...,\n",
       "       [279.64423, 279.542  , 279.47067, ..., 279.77545, 279.8155 , 279.86526],\n",
       "       [279.83334, 279.6843 , 279.56104, ..., 279.0776 , 279.01532, 278.93643],\n",
       "       [279.9746 , 279.82288, 279.6683 , ..., 278.4002 , 278.3426 , 278.20987]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-01-01T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index style selecting\n",
    "ds1.tas.isel(time=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------------\n",
    "Another easy way to select the data by specifying the date as a string using `sel` method. This is label based selection which is possible thanks to `coordinates` in our data. It works on the principle of python dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:57:56.120937Z",
     "start_time": "2019-06-21T13:57:56.061125Z"
    }
   },
   "outputs": [
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
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       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
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       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "\n",
       ".xr-sections {\n",
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       ".xr-section-item {\n",
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       ".xr-section-item input:enabled + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
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       "  font-weight: 500;\n",
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       "\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",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
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       "\n",
       ".xr-array-wrap {\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",
       "  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",
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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",
       "}\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",
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       "\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",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
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       ".xr-preview,\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, 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;tas&#x27; (lat: 41, lon: 61)&gt;\n",
       "array([[290.75778, 289.63876, 288.34048, ..., 287.10742, 287.49075, 287.80527],\n",
       "       [290.72467, 289.38992, 287.91345, ..., 285.26715, 285.9566 , 286.85046],\n",
       "       [290.2865 , 288.90448, 287.25934, ..., 285.4207 , 286.54886, 287.682  ],\n",
       "       ...,\n",
       "       [283.21   , 283.16342, 283.18152, ..., 286.4937 , 286.8491 , 287.3354 ],\n",
       "       [283.37347, 283.232  , 283.19907, ..., 286.67203, 287.06198, 287.55432],\n",
       "       [283.63815, 283.44315, 283.32687, ..., 287.4437 , 287.76877, 288.1402 ]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-05-15T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0b499fc1-5b80-4049-88cd-afd70279792d' class='xr-array-in' type='checkbox' checked><label for='section-0b499fc1-5b80-4049-88cd-afd70279792d' 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>290.75778 289.63876 288.34048 ... 287.4437 287.76877 288.1402</span></div><div class='xr-array-data'><pre>array([[290.75778, 289.63876, 288.34048, ..., 287.10742, 287.49075, 287.80527],\n",
       "       [290.72467, 289.38992, 287.91345, ..., 285.26715, 285.9566 , 286.85046],\n",
       "       [290.2865 , 288.90448, 287.25934, ..., 285.4207 , 286.54886, 287.682  ],\n",
       "       ...,\n",
       "       [283.21   , 283.16342, 283.18152, ..., 286.4937 , 286.8491 , 287.3354 ],\n",
       "       [283.37347, 283.232  , 283.19907, ..., 286.67203, 287.06198, 287.55432],\n",
       "       [283.63815, 283.44315, 283.32687, ..., 287.4437 , 287.76877, 288.1402 ]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e154d7f9-f370-4679-9651-17555e27b9b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e154d7f9-f370-4679-9651-17555e27b9b9' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-05-15T12:00:00</div><input id='attrs-cef56704-7c84-49fa-8dea-8bd34cef13ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cef56704-7c84-49fa-8dea-8bd34cef13ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa58f04-0df5-4995-a391-7bbecc78f0df' class='xr-var-data-in' type='checkbox'><label for='data-0fa58f04-0df5-4995-a391-7bbecc78f0df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2071-05-15T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.1 5.2 5.3 ... 10.8 10.9 11.0</div><input id='attrs-1c8d230e-c5a9-4e9f-a390-bb73d62aee7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c8d230e-c5a9-4e9f-a390-bb73d62aee7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2262c05-796f-408b-8152-2d8769bfea92' class='xr-var-data-in' type='checkbox'><label for='data-e2262c05-796f-408b-8152-2d8769bfea92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,\n",
       "        6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,\n",
       "        7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,\n",
       "        8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.1 44.2 ... 47.8 47.9 48.0</div><input id='attrs-7540f55e-ba3d-452b-a037-42ad6e0c52af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7540f55e-ba3d-452b-a037-42ad6e0c52af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c45beef-78bb-4fc6-b3e4-f588fe973efa' class='xr-var-data-in' type='checkbox'><label for='data-7c45beef-78bb-4fc6-b3e4-f588fe973efa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,\n",
       "       45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3,\n",
       "       46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5,\n",
       "       47.6, 47.7, 47.8, 47.9, 48. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee62afd2-5346-45c4-93ae-a675b2a45bad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee62afd2-5346-45c4-93ae-a675b2a45bad' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (lat: 41, lon: 61)>\n",
       "array([[290.75778, 289.63876, 288.34048, ..., 287.10742, 287.49075, 287.80527],\n",
       "       [290.72467, 289.38992, 287.91345, ..., 285.26715, 285.9566 , 286.85046],\n",
       "       [290.2865 , 288.90448, 287.25934, ..., 285.4207 , 286.54886, 287.682  ],\n",
       "       ...,\n",
       "       [283.21   , 283.16342, 283.18152, ..., 286.4937 , 286.8491 , 287.3354 ],\n",
       "       [283.37347, 283.232  , 283.19907, ..., 286.67203, 287.06198, 287.55432],\n",
       "       [283.63815, 283.44315, 283.32687, ..., 287.4437 , 287.76877, 288.1402 ]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-05-15T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ds1.tas.sel(time='2071-05-15 12:00') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:57:56.137173Z",
     "start_time": "2019-06-21T13:57:56.123811Z"
    }
   },
   "outputs": [],
   "source": [
    "# assigning a variable name to our data for easy access\n",
    "data_to_plot = ds1.tas.sel(time='2071-05-15 12:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:57:57.156634Z",
     "start_time": "2019-06-21T13:57:56.142132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f719768fb50>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# ploting it, just one line\n",
    "data_to_plot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, **we see how xarray makes our life so much easier for quick analysis.** We didn't have to specify a map or axis. It does everything intelligently in the background for us. This saves a lot of time for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just one of the many powerful features which xarray offers.\n",
    "\n",
    "**You can also do everything above in just one line using Python's powerful Object-oriented language features** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:57:58.123620Z",
     "start_time": "2019-06-21T13:57:57.161781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aS+onG3hON7Ov5snHAtPvv0xmK8fMtpjZtvz9OUB/bqJo5lZgnaTpica+wLWzPdduEoNPEATBLDHElFV7dSK3z5wGXG5mH2j66Drg8fn7w4Er8vx3z8uQK+BqZIPNXf0zM+CHZDZ2yAayVpv655XwCxEEQTBLDLrpXudQ4EXAJZI252lvBV4OfCifvYwCx+WfPQt4paRJYAR4bj7YIOkc4GVmdh3wT8CZ+fLdRWQDXEskPQY40Mw+LWlPYJWZ/bFbJznN8hp8VBQUeNc50FlgkBIXLJSYAMpG5/JxIhaLy0MqjzPayueZjeAgNY9OGN874tpOXktXb0pwULIDl45T9XbOU3cCgypGfS/6mJps/znA1Li7DqMJ4YW7b74ntqZs9S8JJG4eLOVZdX2x3hU3ueudEGJM1b0IpJSlJELwl7eSIjmVxz++fe7aVQgbNDvUtXg+ZvZTWnvqOiSR/z+A/2hR1xFN768kX6rrhKR/ATYA9wc+DfQDnycbGLvK8hp8giAI5hEjHchwCfNMMpXdtOT7Okmr2xeZHTH4BEEQzIFlFsl03MxM+XKDpJ7t3I/BJwiCYJaYabnNfL4k6eNkCrmXA/8AfKIXDcXgEwRBMAeWSxjtXDn3ReAgYAuZ3eckMzu3F+0ts8HHCsbpVGyeTgKD2YgLUuWqxEzxnggmJ8qBd6b87n4vDJhIPPiTztiaEByULk1j5gbaUlyblDcAf0qpv9NOqxYp7YMXjqQEBxW8V3SiyvPgRSBK9bcbG+wTz1BtophWH3HPVKMYlwdgaqh4c1deV6536DYvMPB9KXevb8wlJhxndfIyUWqHsqeK1He9fKgj70CiR+NDFkwuFTBr6ZEvt51jZg8BejLgNLM8huwgCIIFIBMcdGefzyLhQkl/OR8NLbOZTxAEwfyyzALF/RXwAkl/AraTzSHNzB7a7YZi8AmCIJgl0x4OlhFPnq+GYvAJgiCYA1PLa+bTM9/1nhh8giAIZokZVQPFLRW+STYACRgCDgB+Bzyo2w0tv8Gn6TlIqY46qdtS6qaUUq1THj8V94oogMaEd7lSVs3YuFNSOZcrtYTaTd6FSULt1un3TRXlmndpY/2JMn1VZHMdlHYpzznunJKhefzT7e91Sv3mVVJV3Be5e+TVhgDyaf4wJdbzboYmynnq7nnoGyl+3r81de+LN7J/WzmLx7urqY+XO1yKqTOWUJu6pCn3TDUGEm6GnGAvda38M1+bLGbq3+blcN3BEJP+JJYwudJtJ5IeDryqF20tv8EnCIJgHllmHg4KmNmFkv6qF3W3HXwknV2hjtvM7MXd6U4QBMHSYVpqvVyQ9PqmwxrwcGYQtnsmdJr5PIA8smgLBPxn97oTBEGwlFh27nWanYhOktmAzupFQ50Gn382sx+1yyDpHZ0akVQHNgHXmtmRkn7CXSd5N+B8Mzs6Ue5Y4G354bvN7LOd2gqCIJhPppbXsttlZvbl5gRJzyaLoNpV2g4+ZvalThVUyQMcD1wOrMnLPHb6A0lnkYisJ2l3YDq2hAEXSDrbzG5v1YhUjK2SMih3EhikRArenJ0SIDRcPJaGc4vTGE+ICSa8mKD8C8q7TykdjyeM2962mrLGe689Q8VMUwMJ8YATFHgxgRIihVLvEtfOfH+9oT1lwK+iY/BuWeq+/4kLM+AqTl07d5/qW5wBP2Hk799aPK70dTUb0WuFiv3zkfrh7gUGvi+1hPihf3ux4r7t5VhCtfHi9bV+93cymHAx1e//BhIxlsaKbWui2E59+1i5w13ADCaWkeAAOJHyQJNKmzOVBAeS7ge8CbhXcxkzO7xC2X2BpwEnA693n60hCwv7kkTRJwPnmtlted5zgacAZ1TpcxAEQa9ZLptMJT0VOALYR9KHmz5aQ7b81nWqqt2+DHyMzLX2TDWLpwJvpriWOM3RwPfNbEvis32Aq5uOr8nTCkg6jjysbN/6tTPsWhAEwdxYJstu15GZRo4CLmhK3wq8rhcNVh18Js3sozOtXNKRwE1mdoGkwxJZngd8cqb1NmNmG4GNAEP32WfeducGQRAsF7Wbmf0a+LWkL5hZYlG1+7SVaUjaPbe9/F9Jr5K093Rant6JQ4GjJF0FnAkcLunzed3ryeKKf7NF2WuB/ZqO983TgiAIFg1TVqv06oSk/ST9UNJlki6VdHyefrCkX0jaLGmTpEfk6S+QdLGkSyT9TNJftKj3M5L+mJffLOngNt3YX9JX8j5cOf2axWXpSKeZzwXc5WoBMrvPNAbcu11hMzuRzFhFPvN5o5m9MP/4WcA3zCwR9QOA7wD/Kmm3/PhJ03W1o9ZkVPbeDLJ+tBcYpOKueIGBFxcANFwsnpLAYCwhJnBpmij/gqr5ndsuT0lckCAVbsQLDBrDzhA82DkWUkmckfoBWLqeqa387tgJDmqjnePY+OsEFWK8pPrrPQ8k6q2PuwR3qWqJe9Ln7N1VBBOVBAcuT2OweOy9A0D5viU9Brj+DWwrJgzcUT7JwduLJ1nb4S8UMOmes+FiB2sjCY8d7o+ylhIPjLT6Gsmp90gU0N1wCZPAG/KNnavJRFbnAu8B3mFm35J0RH58GPBH4PFmdntus9lI5pU6xZvM7CsV+vBpMqHXB4EnkNnje6Il76R2OwBA0pAfJCQNzbHt5wKnuDo3AK8ws5eZ2W2S3gX8Kv/4ndPigyAIgsWAAZNd2udjZtcD1+fvt0q6nMzObeRKYWAt+aZPM/tZU/FfkK0OzZVhM/u+JJnZn4C3S7oAOKkLdReoavP5GdlO105pLTGz84Dzmo4PS+TZRNOmVjP7FPCpqm0EQRDMJzO0+ayXtKnpeGNusy4haX/gYcAvgROA70h6H9ks5NGJIi8FvtWm7ZMlnQR8H3iLmbXSno9JqgFXSHoNmaljVZt6Z00n9zp3Jxt5hyU9jLsWJdYAK3rRoSAIgqXEDAafW8xsQ6dMklaReRU4wcy2SHo38DozO0vSc4DTgCc25X8C2eDzmBZVngjcAAyQLc39E/DOFnmPJ/tufy3wLrKlt2MrnNuM6TTzeTLwYrLp3Pu5a/DZAry1Fx0KgiBYKnR7n4+kfrKB53Qz+2qefCzZoADZtpdPNuV/aH78VDO7NdnHbDkPslnNp4E3tmi7DhxjZm8EtpHef9k1Otl8Pivpv4DnmdnpvexIEATBUqRb+3wkiWxWc7mZfaDpo+uAx5OZLQ4Hrsjz3xP4KvAiM/vfNvXubWbX5/UfDfwmlc/MGpJazZ66Tkebj5lNSXodsPgHH6UVbu1Iqds8/peNd50DndVtGk2o3ZxrnPpYBVc5FfCKJ69kAzDnPqfkKicVBsap0EpucFIKrgq/CkuiuVK9CXc129ofQ8IFjO9+Qgnmy6TcyKjhXBF5VzQJ+/PAFufuxQnBGgPlMpND7rkbLF+HKfcXbBWsuDXXdv+O8h9BfayYNujUbQN3lE0GtRF3saYSD0Td3dvxopzQK9uS7BgpJdm27cV6Bt0fwRp33C2sq/t8DgVeBFwiaXOe9lbg5cCHJPUBo+Sb6slEAHsAH8nGFSanl/UknQO8zMyuA06XtCfZX8Bm4BVt+nBRHs3gy8DOi9o0C+saVQUH35P0RuCLrkOhPguCYJfFgMnE1otZ1WX2U1p75zskkf9ltIg6YGZHNL3v6AatiSHgVrIZ1s4qyGZYXaXq4HNM/v+rm9I67vMJgiBYziwX327TmFlP7TzNVBp8pvf7BEEQBEVSXu6XKrkT6Y8Ce5nZg3NBw1Fm9u5ut1VpviipX9Jrc7cLX5H0mlyVEQRBsEszhSq9lgifIJNmTwCY2cVkDgG6TtVlt48C/cBH8uMX5WntopwuON6VDpR/pfg8Kdc5U41i2tREOY9NekNqMU8q7k7duY3xhmAouz7xrnK8uADKsXiScWtckvw5pbzgNNoLDFKuUbxhPSWgaAz54879L8V4qeBep9TfhJigb6TYVv+OhFjDu+lxRnQvAsjqca6JGv6cEs+d+3mX0p6UHnF3HVJfeb4vXgwB0L/NxebZVrxYJXEBlAUGKdWKT/NlUqIVd31ZMVyudqCo2LCh4sVrrJ6rQ5Y01l3BwWJghZmdr+J9WtCQCn9pZs1O634g6de96FAQBMHSQckfrEuYWyTdh/znp6Rnkbv86TZVB5+GpPuY2R/yDt2bmcf1CYIgWHYsJ5sPmahsI3CQpGvJnJe+oBcNVR183gT8MHetLbKIpvOmigiCIFiMLJd4PtOY2ZXAEyWtBGpmtrVTmdlSVe32fUkHAvfPk37XxjFdEATBroFV26i+VJC0B1lIhccAJumnZBEFkq575kLVmQ9km5z2z8scLAkz+1y3OzQXhCVFBu2Ycuu1XlwA0HDGeEt4OOgkMPDiAoC6G75TBnAvMPBGaC8uyNK84bdcbykGjRdMpGIL+XPyO+UTwdD7XJiVlBeEKXeO5jwGpK6L34GfyjOwtdhY//biSvGUN2QDtclivfXRsq11ckXxz2ZqsLNYo9Nj2TdavjD+nqQ8YHSKUWS1hHeIHe2vC0D/1uLNLQkMGhVW3ZNuMtyFqLlrl7gnvowNlL+2bEVRcDCxtigwmFzVo3g+LJsw2tOcCfwY+Lv8+AVkzgWe2LLELKk0+OT+3e5D5pph+qkzYFENPkEQBPOJsexsPnub2buajt8t6ZiWuedA1ZnPBuCBZstpghkEQTBXRCPhe3AJ811JzwW+lB8/iyyqdNepqhH8DXD3XnQgCIJgKWOmSq8lwsuBLwDj+etM4B8lbZWUWFifPVVnPuuByySdD+y0VJjZUVUK53EiNgHXmtmRuWvvdwPPJlvG+6iZfThRrgFckh/+uWp7QRAE84HZ8lp2M7PV89VW1cHn7XNs53jgcu6KQ/5iYD/goDxkw91alBsxs4Pn2HYQBEHPWE5Sa9gZoG5/msaHBQupYGY/ave5pJ+b2aNafLYv8DTgZOD1efIrgeeb2VRe/02Ve9yun8x8eusfnJTabcqr2xLudbw6zCvDUmosn2aJRVAf52VyhXMHNJRw/5JI88gpqfw51UfK13HATboH7iwe942WTYJePZZSu3k3OL5M30i5UN+Oogqtvr3sm6i2xcV92b6jcGh7rC2VaaxyblgSarGSyyOXJfUENpyCz7sHSrnx6XMqtJRishNlNz5Qmyi2VRsrK9d8nJ2Suq2KGxyvZEvhzcgVzMrWl1CkrijKQKcGnIrVx1zqIsvJEi7pU8BDgUu56y4vaEiFTrRznHQq8GageTp3H+AYSc8EbgZea2ZXpOqVtInMt9ApZvb17nQ3CIJg7hgqbddY4jzSzB44Hw1166olx35JRwI3mdkF7qNBYDSPuvcJ4FMt6r1Xnuf5wKm5zyHfxnGSNkna1Lhze7mGIAiCHmIVX0uEn0ual8GnWzOfVhwKHCXpCLLZ0RpJnweu4a5p3NeAT6cKm9m1+f9XSjoPeBjwB5dnI5kvIobuu88SusdBECx5lpnggGzv5s8l3UAmLhNgZvbQbjfUrZlP8uqb2Ylmtq+Z7U8WE+IHZvZC4OvAE/Jsjwf+t1ShtJukwfz9erKB7LIu9TcIgqA7dGnqI2k/ST+UdJmkSyUdn6cfLOkXkjbnqzyPyNMl6cOSfi/pYkkPb1HvIZIuyfN9WEq5ntjJaWQhc54CPB04Mv+/61T1cLCSTHk2lUe6Owj4lplNm8xfNMN2TwFOl/Q6YBt5XCBJG4BX5LHJHwB8XNIU2SB5ipnNefDxz4C5DWKWMqR6EYI31lM24JfiyyQePh/3JRWbZ3JVsWDJdU5/quIqrnKcO6CxzgIJ705n0Luv2Va+eJW8HU21j3VTH0/UO9lZVGHDTq3hYrxMDbnPAesvXpfGYNktS2OomGdyRWf3Ol5M0hjw96T8OzBhV09U3P7jvsR1UqOYppTF3H8/1YvXwUr9B+t3eeqJE/BxpFz/aqOpoFbubzQhZJhY5QUH3r1V5+dltnRx5jMJvMHMLpS0GrhA0rnAe4B3mNm38lWk9wCHAU8FDsxff0UWY+2vEvV+lGz/zi+Bc8gGlm+16MPNZnZ2t06oHVWX3X4MPFbSbsB3gV8Bx5C72jaz33SqwMzOA87L399BpoDzeTaRD0Rm9jPgIRX7FwRBsCB0S+1mZteTx84xs62SLgf2IRuyp7eprAWuy98/A/hc7nnmF5LWSdo7rwcASXsDa8zsF/nx54CjaT34XCTpC8D/pbinc8HUbjKzHZJeCnzEzN4jaXO3OxMEQbCUMAPrgdpN0v5kNu5fAicA35H0PrIp8qPzbPsAVzcVuyZPaw7+tk+e7vO0Yphs0HlSU9qCSq0l6VFkM52X5mm9cxMbBEGwRJjBzGd9vnVkmo25YKqApFXAWcAJZrZF0ruB15nZWZKeQ2aX6bqXaQAzm7c4bVUHnxOAE4GvmdmleSTTH/asV0EQBEuF6oPPLfnWkZZI6icbeE5vWuo6lsxLDMCXgU/m768l8xQzzb55WjPX5unt8jS3fz8yG9FeZvbg3NvBUWb27nb9ng0z8XDwI0kr8uMrgdd2uzPdoNNz4I2DpQ3WCQ+11vBigoQB3xn1Szv5EzbJiVXF48aKhLeCQZfmxQSj5Sm/j/vSl/BWUHeb/33/+vznwPAtxYvlBQZ9I+Wd8j6eTCq+TCd7rSVivDRKMXUSE3H/MLhqqogJfKyhrFyxoskh570gsZt+ymkbfFymiZUJjxLbOohYoBzrxl3fyeGEkGG0eFL9W8sVywkv/PPsPQgATDmFRMq7QqkdJzZJ6m+d4KAxVP7a8jGVGs6DhNV7tQuje05DcxXaacDlZvaBpo+uI1MFnwccDkxvyD8beI2kM8mEBnc223sgsyNJ2iLpkWRLeH8P/HubbnyCLHL1x/PyF+c2oIUZfPIlt9OAVcA9Jf0F8I9m9qpudygIgmBJ0b1x7VAy5fAlTTb1t5Ip1T4kqQ8YBY7LPzsHOAL4PbAD2LlkJmlzk1/MVwGfIbPnfIvWYgOAFWZ2vlNjp372zJmqy26nAk8mG2kxs19LelwvOhQEQbBk6OImUzP7KS32TJJFkvb5DXh1i7oObnq/CXhwu7Yl3dPM/gzcknuSsTz9WRQFDF2jsocDM7vajYYV4ugGQRAsc5aHh4OvAw8HXkO25HaQpGuBPwIv7EWDVQefqyU9GrDcIDYdIiEIgmDXZnk49RKAmf0BeGLuWKBmZlt71WDVwecVwIfI9OHXkm00TU73FpQqU+CSB3fv4SARLsF7L0h4DPAGWXOGam9gBpgaLhaylLcC345ru297ub/97nHp31aup3+7vxDFw5Rx2xuHJ51xvrxrn5KxuGRgBurjzqPBqAslMJLojFeKJDyGlIzvK4s3wYsLACaHnXggIXbw97LhBAdju5WKMLZ7++vdtz31TLlrl1xraB/PwZ8PwPhq9zyvK6sq/PNcc+IBJW6JD4VRyauA736jc5mJ1QnBgTsFLwrZvlsPPU8vj8FnH0mpgJ4AmFnXBWZV1W63kHszCIIgCHKM5bLsNgL46AM9parabd6030EQBEuJZRJM7lYz++x8Nlh1LvoJsk2mE5Bpv8m8VAdBEOzaTKnaa3GT8OjaW6rafOZN+x0EQbCUqOS5fZFjZo+c7zarDj7zpv0OgiBYMiyxMKWLiaqDz6vJooU2a78XpwChw4NQUsN5tVsV1zmJPKVqnXItpXbzZVL1+kw+T8mND1AfLR6XlG2JtL7R9jF1oIWarbmOkXJnvAKqlojN07el2OHaVncCI+4YygvtfWXFlq1cUTieGijmaQyUH/+RPYor0RMry037uEvjexTPaWpdeVFgYGVxVWNyvNiX0e3lB2TK9a8+VspSutcl1WIyLpOLdZO4rV6BOFWK71Mu4xVmXgUICZWc/xtIPM/+WUzNNKacO52JVU7pWHwUuoiWi+Bg3uk4+EiqA68ys3nRfgdBECwplsnMJ/+uv9TMDpqP9joKDsysATwmf789Bp4gCIImuhRGe6HJv+t/J+me89Fe1WW3iySdTebOe/t0Yi+i2wVBECwZjKWgZJsJuwGXSjqf4nf9UT5jalNqgi1m9rbUB1UHnyHgVjJ33jv7Q8Xodvl0bhNwrZkdmbsOfzfwbDIfcR81s9Tu2mOB6Y6/e7516EEQBJ1YDmq3Jv6/GeR9BnBShzxv4a7v8AJVPRzMNbrdtC+46TjkLyYLgnSQmU1JupsvIGl34F+ADWQD3QWSzjaz29v3VU3v238OYN7AmfgVo0aFXzY1Z6B1VzYZT6SCsbW+o7gy2re9+Hkq7o43MqdcxPjYOwN3Oktwygjd136Vtj5aNrT3bSlayXXbllIe27GjeDxePAGbSPlycS5iViQsymuKaoHGoBcTlE/Su8YZ3TMRY2ltsT+Dq4rnuGq4rAwYqBevd8O5cRpbXbbgT6x3ooSRgVKeqa1FoYLG/PNSvmcDbf+CdtZUPOoQNwjKLqUGEgv0ct/UXqQwuntnVz9eXAAwtraYNr6m+HlK8NM1ltHgk8duq8oHO00IJCWcTWVU9XCQml7dCWwys//uUHZf4GnAycDr8+RXAs83y776zeymRNEnA+ea2W15PecCTwHOqNLnIAiCoBqSfmpmj5G0leJwKrLoDWsSxc5qU9+RZvYNMzu1VZ6qHg6GgIPJIuhdATyULBzrSyW1rDznVODNQPPvl/sAx0jaJOlbkg5MlNsHuLrp+Jo8rYCk4/J6NjW2bPcfB0EQ9BRZtddixsymRWWrzWxN02t1i4EH4FxJ+/tESf9A5oi6LVUHn4cCTzCzfzezfweeCBwEPBN4UqtCko4EbjIz77BuEBjN45l/AvhUxX6UMLONZrbBzDbU1yQ2ZQRBEPQSU7XXEkPSSkkvlPTNFlleD3y3efIg6UTgdWRhv9tSVXCwG1kI7Tvz45XA7mbWkJTY+raTQ4GjJB1BNntaI+nzZLOYabHC14BPJ8peCxzWdLwvWQzzIAiCxYFRXNNZ4kgaIDOTPJ/M9HEW8LFUXjM7J//+/5ako4GXAY8AHtfJNg/VB5/3AJslnUe2Bvg44F/zTaffa1XIzE4kc0iKpMOAN5rZCyWdAjyBzFPC44H/TRT/Tt7GtMHqSdN1dZNKIXDdlHkqFXen7g2yropa53m3GolYQi6Gi9/lPnAnJQbvLP411BNeBVZcWzTy124pVjS1bnWpjPU7Y7DvbuqP0Ks+6olz7HfWYLk8A2Vrsfrcozs0WMrTWD1UPB5ObMv3ZVw12qP822rNmqLKY+1w0c3A6v5ymSlnwJ/0goNGIkaNezYbK8qeHhouTs3WkeIJjG4vixQm1havZ22sfE+8aMWLblKeE2rONeXEivLfVn3M1eNjFKVky657o3uUs0wOF4+9+KGXE4/FvqRWBUlPAp5H9j37Q+BzwF92EpuZ2fclvYRsYvAz4HAzS7gkKVNV7XaapHPIRjWAt5rZdfn7N1Wpw3EKcLqk1wHbyEZMJG0AXmFmLzOz2yS9C/hVXuad0+KDIAiCRUOXBh9J+5F96e+V17rRzD4k6YvA/fNs64A7zOxgSS+g+P37UODhZrbZ1ft24OXAzXnSW83sHNf8t4GfAI8xsz/m5drabZrECSIzpfw1cFO+laaVSGEnVdVuyiu+t5m9U9I9JT3CzM6vUp6sJ+eRL5uZ2R1kUzufZxP5QJQff4o52IOCIAh6TvdmPpPAG8zsQkmrybaXnGtmx0xnkPR+cvOHmZ0OnJ6nPwT4uh94mvigmb2vTdsPJwuT8z1JVwJnkvTgdxdmVl4emQFVBQcfAR5FNi0D2Ar851waDoIgWOpUVbpVWZozs+vN7ML8/VayvZE7Fb75JOA5pLebPI9swJgVZrbZzN5iZvch2195MNCfq5GPS5WRdGGnetvlqWrz+Ssze7iki/KO3p4bpoIgCHZtqrvXWS9pU9PxRjPbmMqYS5gfBvyyKfmxwI1mdkWiyDFkHgda8RpJf0/maeYN7QQBZvYz4GeSjidTNj+XLKqB5wGSLm7TpoC1rT6sOvhM5C5ypuP57Mmy0ngEQRDMjhkIDm7Jt5e0r09aRaYyO8HMml2CPI/ErEfSXwE7zOw3Lar8KPAusu/vdwHvB/6hUz9yJwDfzV8pqni/9pKSnVQdfD5MJom+m6STgWfRwl/PQtPsvqOSks2X7yuPqVM+LklisdIS5Qr1Jlz0eFcoXi2UlXPtVDilvtFiX4av31HKU7vTpdWLy7u17QnBilOqTa0sKqsaK8qqtMaqYlp9OKFcm3An6WLHWK18wc25+rH+cp7JFcXH28cjGl9VvpiTq4rfJKvXlP0X7TZcTFs94NzrJNRunpr7xvLKNoDJqeI98Yq5VLlRd87b15QXKEbXF+/B2GTCtY9Lm5go1js+Xi5jTq1p4wllo4slVHN5Ui6mrM/F80nGvXKHTpHq1addpYtqN0n9ZAPP6c2OmyX1AX8LHJIo9lzaeH4xsxub6vkE8I1u9NXM/jSX8lXVbqdLuoBMdCDgaDO7fC4NB0EQLHm66L0gt+mcBlxuZh9wHz8R+K2ZXePK1MjsQI9tU+/eZjYdefqZQKsZ0rzSdvDJnXtOcxNNo6uk3UP6HATBLk/3Zj6HAi8CLpG0OU+blkW3mt08DrjazK5sTpT0SeBjuYL4PZIOznt6FfCP7Toh6THAgWb26dzEsmpaft1NOs18LuAuHfc9gdvz9+uAPwMHdLtDQRAES4ouDT5m9lOS/uTBzF7cIv084JGJ9OYtKy+q2gdJ05EE7k/meaYf+DzZwNhV2q6EmtkBZnZvMi8GTzez9Wa2B3AkrY1QQRAEuwzLwbFoE88EjiIPJJc7E5jTfp5WVBUcPNLMXj59YGbfkvSeXnSomyQFB50s9ik3OMMtBRs7ka/Wyy8nEq5GRopjfz1l43ciBP8QezciWabiYUoIgBX9kdR2uIZ2JAIFeYacMbt0EaAx6P2cpOopPoZTroyPwwPlmC6p2zq5wrmwcTFftu1f7ozu3t51DsDawWLair7itRuql33P1N2N63NKEi9AAKi7i9VI/yhuixctQFm4MJHIM9rod8fFe7RtIiFkmCyWGZkoP3ej404EMjVzJcDEaMIV0ZhL83/HvfTruXQGliqMm5kpV27lLtR6QtXB5zpJbyObfgG8ALiuTf4gCILlz9Ka1VThS5I+DqyT9HIySfYnetFQ1cHneWS7Xr9GNs7/mLu8HQRBEOy6LKPBx8zeJ+lvgC1kdp+TzOzcXrRVVWp9G1ko7CAIgqCZZTT4SDoA+Mn0gCNpWNL+ZnZVt9tqu+Cae0NtS5U8QRAEyxGx7AQHX6bovaaRp3WdTjOfl0na0uZzkenP3961Hs0BAxpNu6wt4VXAixCs5JepczyRZNtu13VpF3aiL7VJ13JK1+AeWl+mNlF+qq3m+jJR3jZeit9zw03FMgNlg7L23bt4PFnsTN+d5b7UnCHY+srGbe+JYHK4eMFTgoPJ4eI5br97+fqO7OUM9muKQoDBdWVPBHusLoZi322wLLzwAoMBF5TGiwuyPMVrNeiO+xM334sQUnnqKZcAM6SR2P4/4ZQsY1N9bY8BRpxIYcdkOcbSloliWsqzg2eoXrxWXvwAsGWsGLtp+1jx+fUeG7qGpb0yLGH6zGznA25m473y49lp8PkEnWV2PTFGBUEQLAmWzqymCjdLOsrMzgaQ9Azgll401HbwMbN39KLRIAiCZcPyGnxeQRbo8z/IloGuBv6+Fw1VVbsFQRAECZaQPacjZvYH4JG5Z23MbFuv2pqXwScPx7AJuNbMjpT0GeDx5BH5gBenIvBJagCX5Id/NrOj5qG7QRAE1VlGgw+ApKcBDwKGlG8cN7N3drud+Zr5HE8Wla85pvebzOwrHcqNmNnBPetVEATBXFhmggNJHwNWAE8APkkWPuf8XrRVafCRdD+ygER7mdmDJT0UOMrM3l2h7L7A04CTgdfPpbMdsaLCbWoyFXjHqWsqRCE0/8sm8UtHPi6JV78l1G6NAe8rp7Mirs+Jr4ZvKz/5wzcUM9Vv217KQ39RmVTbY/fi507JBsCtxeCH6nOPz0DZnUp9zKmbVg2V8si50xndrXi8Y6/ydZlYUzwe37Pc34Hdi25w1g27uDuDZbXbaheLxyvZIO0KZ6Z4RVyqTq9u65XarZZ4oH29vm2v1gNY4dwKrfK+oYC1/RXcNjn6vJow0d/Gaqd+dEq7bQnl3W9n3JMWLK+Zz6PN7KGSLjazd0h6P/CtXjRU1bHSJ4ATgQkAM7uYTGJdhVOBN1OOfHqypIslfVBS+cnIGJK0SdIvJB1dsb0gCIJ5Y5nt85n+xbZD0j3IvvP3bpN/1lQdfFaYmZ96JX4aF5F0JHCTmV3gPjqRLATrXwK7A//Uoop75WFnnw+cKuk+iTaOyweoTY2tiV/4QRAEvcQqvpYG/1fSOuC9wIVk8X++0IuGqg4+t+Rf/NOeTp8FXN++CJDFgDhK0lXAmcDhkj5vZtdbxhhZzIhHpAqb2bX5/1cC5wEPS+TZaGYbzGxDfXXPHLAGQRCUqTrwLPLBR9Kz87efN7M7zOws4F7AQWZ2Ui/arDr4vBr4OHCQpGuBE4BXdipkZiea2b5mtj/ZMt0PzOyFkvaGnWFjjyYR1lXSbtPLcZLWkw1kl1XsbxAEQc/RDF6LnBPz/8+aTjCzMTO7s0X+OVPVseiVwBPz2A41M9s6x3ZPz8OzCthMtrEJSRuAV+RR+B4AfFzSFNkgeYqZzX3w8Tb+CvZaeVFCylXOWHEc79vR+XGTFxOkdAEubWBLscMDd5Rjx9S2uBg0I4lAQV4sUHfuR6bKF8ZGnYHeHWsoYbpzaVODCbcsdysKFbbvU7x2O/YpG9rlXOWsXp2Iu7OiaNxe2V80gK/oS1w7tzjvY98AjDv3LlMJUUKpXvfgTfh4PrXy9S7F3aFzbJ6UeKATqXP0LnequMGpObPuYL28Mu/FA6W+pEQ3FQwm/qlaMVi8t3sO9my7ynJRu90q6bvAvSWd7T/sxTaXtoOPpKQ6rUn7/YGqDeXhXs/L3x/eIs8m4GX5+58BD6lafxAEwYKwyJfUKvI04OHAfwHvn48GO818pv263Z9MHDA9Ij6dHmm/gyAIlhRdGnwk7Qd8Dtgrr3WjmX1I0hfJvoMB1gF3mNnBkvYn2z/5u/yzX5jZKxL17g58EdifTEDwHDMr7JvIHYj+CviRmf2oO2fUnkq+3ST9GHj49HJbHkbhmz3vXRAEwWKmuzLqSeANZnahpNXABZLONbNjpjPk+26a7TB/qLAR/y3A983sFElvyY9LCmMza0h60JzPoiJVPRzsBTQvmI/naUEQBLs2XRp8zOx6chWxmW2VdDmwD7nQKhdoPQdImi3a8AzgsPz9Z8nMH622t2zObT5fBnZanM3sqzNssyNVB5/PAedL+lp+fDTZSSw+1OJ91eIpjwcVBAelB9Ad9yfsnYO3FY9X3lQ2xvbtcLu7R3wMnbKhXWNuZ3nJRQPY7XcUj8eLZTSc8ETgPRi4mD+2ZlWpTGPdcOF4ZK+yKOH2g4rG7dF7FM9xYLfyOa5aURQ7rBks5/ECgz5n1E8Zt31alTx9taIQYDJhVJ+stxeWTqVi6lSIE+SN/KXPE2XqHcqkaNBZgDBVQTybOoeZUqWOTtelm8xAcLBe0qam441mtjFZZ7ak9jDgl03JjwVuNLMrmtIOkHQRWdjrt5nZTxLV7ZUPbAA30H7iMATcSnGAM2BhBh8zO1nSt8hOHuAlZnZRtzsTBEGw1JjBeHpLvmm+fX2ZR+mzgBPMrDmY5/OAM5qOrwfuaWa3SjoE+LqkB7kyBczMpNY9NrOXdDyLLlHVt9s9yQIKfa05zcz+3KuOBUEQLHq6vIFUUj/ZwHN681KXpD7gb4FDdjadbdIfy99fIOkPwP3IIgg0c6Okvc3s+nyP5U20QNKnSZyRmf3D7M8qTdVlt29yV4eGgQPIFBbzZpwKgiBYlHRP7SbgNODyxDaWJwK/NbNrmvLvCdyWCwXuDRwIXJmo+mzgWOCU/P//btONbzS9HwKeCVw303OpQtVlt8J+G0kPB17Viw4FQRAsFURX1W6HAi8CLpG0OU97q5mdQ+Yh5gyX/3HAOyVNkDlufoWZ3QYg6ZPAx/K9k6cAX5L0UuBPZKKFJLlbnZ1IOgP46VxPLMWs4vnkUsC/6nZnuoK1eL8zTe2PJ8qG1PqIM6Sm9AYdxA31svd+hu5w3gruLO+477/RKRWuu7HYrvc6QPm0U+IBT22V84u3suwnz4aLYgFbURQcbN+vXGbbPj48Qrnt8fXOsL6mKBRYvbIsJlg9UDzv4f7ytfNU2aU/6Qz/k1NlI7qvZ4DOHg48VTwRTCRECB3rdd+EqfADNWchT4oSOnyjNipcyxSd7kEVbwaV2tbMr92s6Z7a7ae0kEmZ2YsTaWfR5A7Hffaypve3An89y24dCNxtlmXbUtXm0+zpoEa2E7YnU7EgCIIlg4GmloeLAwBJW8mGU+X/30BrWfacqDrzWd30fpLMBpQccYMgCHYlllCsno6Y2erOubpD1cHnMjP7cnNC7oL7yy3yB0EQ7Bosg8FH0r3I3PbcmR8/gWw/51XAf5pZOSztHKm6MHpixbQgCIJdimUSyfRLwEoASQeTTSz+DBwMfKQXDXbyav1U4AhgH0kfbvpoDRUimQZBECx7Fv/AUoVhM5u2478Q+JSZvV9SjSzsTdfptOx2HdmGpaOA5lDYW4HX9aJDc6VZ5KJawq2MMw5qwsXh2V6eDPZvdS5XEldtcoVTGTmBVt+Ocpm+kaLqqP/mhA+e691+MOemRSuL7muyDrpzHBwo53HqtqlVxXpsqHySk6uK7nXG1hWPR/YoX7uxdcXj8d3LyrDa2uKMfpVTt60cKM/4B/uKv31S6jGvnKqipPLVpMqU6nWFvBufLK29Im4s8VB5VVpKgeaVXyk3PZ3q7U/4h/F5qlCKhTQLRVyVe5RS8HnG5it829KY1VSh+YIdTr6yZWZT0yF0uk0nr9a/Bn4t6XQzi5lOEARBE2LZBJP7gaQvkbns2Q34AUDuEaHr9h7ovOz2JTN7DnBRyh+QmT20F50KgiBYMiQc9y5BTgCOAfYGHmNm0xvn7g78cy8a7LTsdnz+/5G9aDwIgmCpsxyW3czMgDOb0yQdaWbfaFFkzrRdIG5yw/0qM/tT84sZuNeRVJd0kaRv5MefkfRHSZvz18Etyh0r6Yr8dWzV9oIgCOYFm8Fr6fHOXlZedZ/P31De5frURForjicL97qmKe1NZvaVVgXy0K//Amwgu3UXSDrbh39th6Vi8+wounvp21o8TsXdqY23P857XKx3pPhp32j56Ru8tegiRrfcUcozeUcxrbZiRbFMvdh/ABsvuprRyhXlPENFsYANFutpDCcEB8Muz4D/vFSE8bXuvFeVDe8rVxavw27DxYvXX0+IFCqICTrlSbnOqSIeGKq7eEPuuC9hBOh3ggPfzlQFA/nEVPlee6O+dw+UEiB4McFkUnDgrlUVIUOFc/J5yveoswGlSjyf2br/mQ3LxOaToqcXse0TJemVki4B7i/p4qbXH4GLqzQgaV/gacAnZ9i3JwPnmtlt+YBzLvCUGdYRBEHQUzRV7bXYyVeoftiU9I+9bK/TzOcLwLeA/58s7vc0W6e9p1bgVODNFF30AJws6STg+8Bb8tgUzewDXN10fE2eVkDSccBxAH3r11bsUhAEQRcwlovggDw0w5SktWZ2p5md38v2Okmt7wTuJIugh6S7kcV4WCVpVadgcpKOBG7KAx0d1vTRiWQO6waAjWTLd7NaX8zD0G4EGLz3PsvjKQiCYMmwHAQHTWwjC+lwLrB9OtHMXtvthqp6tX468AHgHmRR8O5FZsPpFEzuUOAoSUeQDVprJH3ezF6Yfz6WR857Y6LstcBhTcf7AudV6W8QBMG8sbwGn6/mr55TVXDwbuCRwPfM7GG507kXdiiDmZ1IvlM2n/m80cxe2BTSVWTO636TKP4d4F8l7ZYfP4kK/uSaZ8A2UTZp1ceKafVx/3m5Tjl7d8r26gUG/oGcHCrb7qzu0hLiAS8oKB37ODyABl3cnVXleD42WBQc4HcxJ9zE1yac54RS4KBSEcw1Ux8s71VeNVS86CXvBV36aekFBmkxQVGsMZDwTLCuv+iuYpV7aFb1leMP9buHyAsB7kyoNba4tJTgwAsBxp2nhJSXgT53r6cSsW+8R4bxRrHelABhvFHsX+q++WvuxRlp4UjnPKUy8zQidDmY3IJjZp+dr7aqOhadyAMS1STVzOyHZCq02XJ6LmS4BFhPNrghaUMegY/cpvQu4Ff5650zsDMFQRD0HrPqryWApAMlfUXSZZKunH71oq2qM587JK0Cfkw2cNxE03pgFczsPPJlMzM7vEWeTUBzBL5PAZ+aSTtBEATzyVJQss2AT5Ntcfkg8ATgJVSfpMyIqpU+Axghcyb6beAPwNN70aEgCIKlxDIJqTDNsJl9H1DuUODtZFtluk6lmY+ZNc9y5m1NMAiCYFFjJO2js0HSfsDngL3ymjea2YckfRG4f55tHVnQt4Ml/Q1wCplqeJxs4/4PEvW+HXg5cHOe9FYzO6dFN8byMApXSHoNmfBrVTfOz9PJseh0PO/SR2TugNYkPguCINh16N6sZhJ4g5ldKGk1mVeXc83smOkMkt5Ptv0F4Bbg6WZ2naQHk4m0Snshcz5oZu+r0IfjgRXAa8ls7k8AeuLarNM+n3mL590tCkKehNLH+opPSmOwmEeJwBE+Nk8qNIsmXIJXuyVcz4zsVVSh1XfsXm67390ip0yy4aKyDRKxeBJr0rVtxZMqqd8a5b8ouV9446vL6qtSO2PF69uYTMRLcu5zvJoppTjzpFRdndRta/rLqrQ1Tqk27OWQwFonbVzh/C0N1vzDAHV3ExpuxXvCytfSq91GG/2lPKNehZZwGZToTIGhRH/Lboac+m2y3N+tE8VncaJRzlO6tyVXReV77e9/SqVYztPenVE36daSWu5L8/r8/VZJl5MNJpcB5Org55DF28HMLmoqfikwLGkwsWF/Jn34Vd7WlJm9ZLb1VKEnhqQgCIJdhupqt/WSNjW9jmtVpaT9gYcBv2xKfixwo5ldkSjyd8CFbQae1+Su0T7VtH0l1e6jJF0G/DY//gtJPQmjHYNPEATBbLEZ+Xa7xcw2NL02pqrMlcVnASeY2Zamj54HnJHI/yDg32jti+2jwH2Ag8lmVu9vc0ankvnVvBV2BhR9XJv8s6aq1DoIgiBwZJtMu7ekJ6mfbOA53cy+2pTeB/wtcIjLvy/wNeDvzewPqTrN7Mam/J8A2sboMbOrXejszuvesyBmPkEQBHNhquKrA7lN5zTgcjP7gPv4icBvzeyapvzrgG+SOWb+nzb17t10+EzSHmWmuVrSowGT1C/pjWSu1LrOspv5WPNNrpV/kVjdxRwZ9HFLUiKF4rEXIKTSak64kLAnM7K7Ew/UyvqOwT2KRue+7cWKp/oSMVMmXYyUkUQAolEXS2hLMZBRPeXqZ8+i1/DaZFnsUOqLa3pipPzIjYwXDel1v2sv8ZR6w/rWsbILIR/5ff81RQcZK/vKy+NeYDDobySdY86MTZWFAV5QsLVR7O/1Y2WP7DeOFJ+HW0bKrpTMPa/+nAfr5f5PmnuGEs/8ir7idfBucFJiDc+W8fLzsW2smNZw93G8kRATuHNY0VcWSJTc9HQQTHSTLs58DgVeRObYc3OeNi2Lfi7lJbfXAPcFTsojBAA8ycxuyj3FfCzfuP+ePGCnAVfRPlTCK4APkQkdrgW+C7x6jueVZNkNPkEQBPNGF6OUmtlPaRHAzcxenEh7N7lrssRnzZ5iXjSDPtwCvKBq/rkQg08QBMGssdIWhKVI08wphZnZu7rdZgw+QRAEc2GJOA3tQMpX50rgpcAeZBtOu0oMPkEQBLPFlodjUTPbKb/OvSscT+ZU9EzaS7NnzTIbfFQ0wE6Vl0/VcGnuR4v3gADQ8LFuErgwKkkvCJ4Jpy/Yvm9ZfDh420DheMXNRWP2wJZyQ/3bnJE5YQCv9bu0cWfEnSgbda2/aDT3IV1SQgx/HUrXH9gyUhYLNNNIxI7xu+cH+sqG9d0Gi54I/O56H/sGYKTCAr430I+5k0zF3RlzgY22TRbv6+3jK0pl7hwvXpexyXJ/G1NecFD8fLJRvnZjzivCSMKrwI7JYn+HnNHfH0PZy8C6wfID4cUCU2kTR1uSXhBcf7yYpL+XI8TymPkgaXfg9WQ2n88CDzez23vV3jIbfIIgCOaZZTD2SHov2T6ijcBDzGxbhyJzJvb5BEEQzAGZVXotct4A3AN4G3CdpC35a6ukLR3KzoqY+QRBEMwWI+mEd6lhlljb7jHz0qCkuqSLJH3DpX9YUnJ6J2l/SSOSNuevj81HX4MgCKoiqs16lsDMZ96Zr5nP8WQuGnbG/5G0AWjpXTXnD2Z2cA/7FQRBMDdiYJkVPR98csd3TwNOJlNSIKkOvBd4PpmvoS5h1Jrc53hXOpBWsxVJqW+cC55EbJ5SMS+qS7jXKfUv0bXGQHFy2hgqVrzmT4n4OFtdVxJ/HDZYvPWacoqz0bLrGesrnsTksIuXM1pupz5S7K+P7wMwtr2o/NrmVGlJ9dh4sf/9fWUF1Mr+oouYHU5hlqJK3Jcpp6zyyjuvqgPoU7F/q5z7mrGpojIPYNSp25Ixi1zb3r1OFbyLG4BtjaIbnG0Uj+uJmDr+vEtukiir3arE6hmqF8sM18tKzGEXk8i7FfLXv6vE4DMr5mPZ7VTgzRRd670GODsPntSOA/Lluh9JemyvOhgEQTArjK45Ft3V6OnMR9KRwE1mdoGkw/K0ewDPBg7rUPx64J5mdqukQ4CvS3qQi29BHpDpOIC+9WXnjEEQBL0k7Dmzo9fLbocCR0k6Ahgis/lcCowBv89jRqyQ9Hszu29zwTwi31j+/gJJfwDuB2xy+TaSadMZvPc+8RQEQTCPGEzFtGY29HTZzcxONLN9zWx/MpfgPzCz3czs7ma2f56+ww88AJL2zG1DSLo3cCBwZS/7GwRBMCOMmYTRDppYVPt8JB0FbDCzk8hCt75T0gTZiukrzOy2duVrNWNw6C5D7ljC+NpwcWpszB2Pl8fjWoUhesoLGfqLxynxg7dtK9G2L2fOf0qjPxV/yKsdUsILd+tXueuwuuzuZXKFM4C7KhoD5b648DgMX59w9zJaNGaPrPLuX0pFYLhoQLZVZYHEFh/jx7l78cZuAFxbqXg4wy5I0Yp6se219bJ4YM++4j69Uefy6LbBVaUy1w+uKxzfMVFWutzp0rZNFK/lZGL7ho+F5I+h7NLIiyp2jJfFGxOTxTwpgYQXhgw5t0jD/eXYUyvcszraV3YXdaf7Y6oiUugaMfGZFfM2+JjZecB5ifRVTe/PBs7O359FFk42CIJg0RI2n9mxqGY+QRAES44YfGZFDD5BEASzxQwSob+DzoRj0SAIgrnQJcGBpP0k/VDSZZIulXR8nv7FJjdjV0na3FTmREm/l/Q7SU9uUe8Bkn6Z5/uipM67reeBZTXzqclYOXiXwTIVhmfExT+Z8vFOJhPG+URcoBI+ixMg1PrLxm2bcLvTE7Fu6s4jwIDzL1sfT4gqhtyO+/Hyba65i2Puj8N7M4By/B6/gT0RHqeE974AIGfTnxgttt0YLp/jpPeCMFju73ZnFPcxX/xue4Bhp5C4+8AdpTz79Rd1L6udwGBdbUepzJ4u2NGEM8Zvt7IR/YaBNYXjrVNlwcGOqeI53jxZDBJ1+8TKUpnbXNrWiXI8pfGEV4lmbh0tC1K2mIs/NFL+jhsdLZ7nVncd6glPFfV68UEb6C+LQFYMFO9lv4v5kxKOdI3uLbtNAm8wswvzgG4XSDrXzI6ZziDp/cCd+fsHkqmIH0Tmkfp7ku5nZv4i/hvwQTM7M/eR+VLgo93q9GyJmU8QBMFsMWDKqr06VWV2vZldmL/fSuYPc5/pz5VtjHwOcEae9AzgTDMbM7M/Ar8HHtFcZ17mcOAredJngaPncMZdIwafIAiCWWNgU9VeM0DS/sDDgF82JT8WuNHMrsiP9wGubvr8GpoGq5w9gDvMbLJNngVhWS27BUEQzDvVl93WS2r20LIx99BSQNIqsm0mJzh3Ys/jrlnPkicGnyAIgtlizETtdouZbWiXQVI/2cBzupl9tSm9jyzM9SFN2a8F9ms63jdPa+ZWYJ2kvnz2k8qzICyrwadWM4abDI9+lzbAaM0Zdie9N4BExV4HMAv7Yq2//ID6lKmyDZcJ19bE1uI59e8oixRqE05MsK5szMYZuPu2F22UjaHEtduj+Lg0BovtpLz5e1GC89QPgLOZY+6pTAoZnKF6arLzCrLfTb/HwPZSnn0G7ygc33vgplKeAwduLhyvc54S+hNhOfpV7F+9lKdsaL97vdiOfxZS3NAoihJubqwp5fGihNsmy94VvAeGUjvD5Xqv3bGucHyVlcN17RhxHhicV4/GePt2AUZrZSHDjsHi9RsYKN7roYEeejjokuAgt8+cBlxuZh9wHz8R+K2ZXdOUdjbwBUkfIBMcHAicX+yamaQfAs8CzgSOBf67Kx2eI2HzCYIgmDUVZdbVBqhDgRcBhzdJq4/IP3subsnNzC4FvgRcBnwbePW00k3SOXkEAYB/Al4v6fdkNqDT5nrW3WBZzXyCIAjmFaNrXq3N7Keko1liZi9ukX4yWaBOn35E0/srcSq4xUAMPkEQBHMh3OvMihh8giAI5kIMPrMiBp8gCILZYoY1EqE5go4sq8GnhjHc5DKlkYhTMjZUPOUdTiVlE+UyJSVVQtalDu50+hLudXBpjYFynkkXu2R7rdj/8XXl/q64wbvXKTftFWb1kWK9XnEGkPDUUqSC2i2liKt1EiIlZDE+ztHAUNl9yqrBYpyd3QeLbm/2HCj7+rnXwC2F44cM3ljKs9qtyg+peLFqSbVbvW2eusonOewUiVOJCzzlNJNr3c3er6/c/4mBYtrNjbJ7nZsaRUXcqOvL7n3bSmVWubhGqThBt/YXH6IdTt02NlF+8Bru7y+lbPRusiac66pa6sHrFhW8FwRlltXgEwRBMO/EstusiMEnCIJgtph1Te22qxGDTxAEwVyImc+smJdNppLqki6S9A2X/mFJ5cXjuz7vGKsiCIJg4cgEB1VeQZH5mvkcT+YefKdPDkkbgLL/jbs+rxqroqkQ9DVZuH38FqAQ7yfFpI/vQ9ngWe8rT7MHXBwSH3OknjB4NpyLmNGEaxFvSG0MumPvHgjYfo9iWi0RysS8VyEXvmWqP6UM8JV0OG7Rdqkvrt7GCnd9Vybit6wuGrfvse7OUp4DVhfj7hwwXBQTPGCo7OLqPv3FPF5cADDkxAPedU4t8ZuuLDjo/LuvlhAheCbMxShyZXxfszLF67tCY6U8e7n4Q7c5dzt71Mu/Ge/Rf3vheDBx868ZXlc4vmO86FNqx2T5b2DEpY1OlPP4yYeP5TXU3yP3OtMhFYIZ0/OZj6R9gacBn2xKqwPvBd7cpmjHWBVBEAQLTg9CKuwKzMey26lkg0zz1X8NcLaZXd+mXJVYFUg6TtImSZvG7xjxHwdBEPQMA2zKKr2CIj0dfCQdCdxkZhc0pd0DeDbw791ow8w2mtkGM9swsK4cZjgIgqBnWG+Cye0K9NrmcyhwVO6ZdYjM5nMpMAb8PvMgzgpJvzez+7qyVWJVBEEQLCgxq5kdsnmSCUo6DHijmR3p0reZWSmgiKQHAV8gs/PcA/g+cGA7wYGkm4E/5YfrgVta5V1kLKW+QvS310R/e8t0f+9lZnvOpSJJ387rq8ItZvaUubS3nFhU+3wkHQVsMLOTzOxSSdOxKiZpilXRiuYHSdKmTlEDFwtLqa8Q/e010d/e0s3+xmAye+Zt8DGz84DzEumrmt6fTRadb/o4GasiCIIgWNpEJNMgCIJg3lnOg8/Ghe7ADFhKfYXob6+J/vaWpdbfZcm8CQ6CIAiCYJrlPPMJgiAIFikx+ARBEATzzrIbfCRdJekSSZslbVro/nRC0jpJX5H0W0mXS3rUQvepFZLun1/X6dcWSScsdL/aIel1ki6V9BtJZ0gqh+1cREg6Pu/rpYvx2kr6lKSbJP2mKW13SedKuiL/v6XD4PmmRX+fnV/fqdzBcbAALLvBJ+cJZnbwEtl78CHg22Z2EPAXZN6/FyVm9rv8uh4MHALsAL62sL1qjaR9gNeS7R17MFAn85S+KJH0YODlZBur/wI4UpL3/LHQfAbwe1veAnzfzA4k2wz+lvnuVBs+Q7m/vwH+FvjxvPcm2MlyHXyWBJLWAo8DTgMws3Ezu2NBO1Wdvwb+YGZ/6phzYekDhiX1ASuA6xa4P+14APBLM9thZpPAj8i+JBcNZvZj4DaX/Azgs/n7zwJHz2ef2pHqr5ldbma/W6AuBTnLcfAx4LuSLpB03EJ3pgMHADcDn86D7X1S0sqF7lRFngucsdCdaIeZXQu8D/gzcD1wp5l9d2F71ZbfAI+VtIekFcARFP0bLlb2avJQfwOw10J2JlgaLMfB5zFm9nDgqcCrJT1uoTvUhj7g4cBHzexhwHYW15JFEkkDwFHAlxe6L+3IbQ/PIBvk7wGslPTChe1Va8zscuDfgO8C3wY2A0sqBKZlezdi/0bQkWU3+OS/djGzm8jsEYs5AN01wDVm9sv8+Ctkg9Fi56nAhWZ240J3pANPBP5oZjeb2QTwVeDRC9yntpjZaWZ2iJk9Drgd+N+F7lMFbpS0N0D+/00L3J9gCbCsBh9JKyWtnn4PPIlsKWNRYmY3AFdLun+e9NdkjlQXO89jkS+55fwZeKSkFcrid/w1i1jQASDpbvn/9ySz93xhYXtUibOBY/P3xwL/vYB9CZYIy8rDgaR7c5f6qg/4Qu6cdNEi6WCyEOMDwJXAS8zs9gXtVBvyQf3PwL3N7M6F7k8nJL0DOIbMM/pFwMvMbGxhe9UaST8B9gAmgNeb2fcXuEsFJJ0BHEYWRuBG4F+ArwNfAu5JFtLkOWbmRQkLQov+3kYWzHJP4A5gs5k9eYG6uMuyrAafIAiCYGmwrJbdgiAIgqVBDD5BEATBvBODTxAEQTDvxOATBEEQzDsx+ARBEATzTgw+QRAEwbwTg88uhqRtPajzKElvyd8fLemBs6jjvJm4t8/z/07SUYnP9m92ob/ckfTWpvfDebiLcUnrF7JfQdCOGHyCOWNmZ5vZKfnh0cCMB59Z8gIzO7uXDUiq97L+LrFz8DGzkTzkxWL23h0EMfjsqijjvXngskskHZOnH5bPKqYD3J2eu6ZB0hF52gWSPizpG3n6iyX9h6RHkzkcfW/+6/s+zTMaSeslXZW/H5Z0Zh5A72vAcFPfniTp55IulPRlSasqnM8hkn4t6dfAq5vS6/l5/krSxZL+MU+vSfpIfj7nSjpH0rPyz66S9G+SLgSe3ao/eZs/yq/Hd5r8m71W0mV5e2e26fNKZcHOzs+9mj8jT99f0k/y9i7MryuS9pb04/za/kbSYyWdQhYyYrOk0yvd/CBYDJhZvHahF7At///vgHPJAqztReYyZ28yVyR3AvuS/Tj5OfAYYAi4GjggL38G8I38/YuB/8jffwZ4VlN755EFc4PMxclV+fvXA5/K3z+UzP3NhjzPj4GV+Wf/BJyUOI+d9ebHFwOPy9+/F/hN/v444G35+0FgE5mX62cB5+TneHcyJ57PyvNdBby5qc+l/gD9wM+APfP0Y5rO5zpgMH+/rs29+FfghdP5yJyIriSLOzSUpx8IbMrfvwH45/x9HVjdfE9d3VcB6xf6eYtXvFq9+gh2VR4DnGFmDTKvxD8C/hLYApxvZtcASNoM7A9sA640sz/m5c8g+2KfLY8DPgxgZhdLujhPfyTZst3/5BOuAbIBsCWS1pF9yU9HpvwvMs/bkDmXfej0rAZYS/aF/hjgy2Y2Bdwg6Yeu2i926M/9gQcD5+bpdbKYQZANhKdL+jqZ37NWPAk4StIb8+MhMv9o1wH/kfv9awD3yz//FfApSf3A181sc5u6g2BRE4NPkKLZ8WaDuT0nk9y1vDtUIb+Ac83seXNo09f3f8zsO4VE6YgO5ba364+khwCXmtmjEmWfRja4Ph34Z0kPsSwyaapvf2cuqqakt5M5wfwLsms3CllUTmXxqZ4GfEbSB8zscx3OIwgWJWHz2XX5CXBMbhPZk+zL8vw2+X8H3FvS/vnxMS3ybQVWNx1fBRySv39WU/qPgecDSHow2dIbwC+AQyXdN/9spaT70QbLQo/fIekxedILmj7+DvDKfLaApPsp88z9P8Df5bafvciWG1O06s/vgD0lPSpP75f0IEk1YD8z+yHZEt1aoJXN6jvA/2myqT0sT18LXJ/Pyl5ENqtC0r2AG83sE2Se0KdjP01Mn18QLBVi8Nl1+RrZ8tCvgR+Q2ThuaJXZzEaAVwHflnQB2SCTCqlwJvCm3IB+H7Iw1q+UdBGZ/WSajwKrJF0OvBO4IG/nZjIb0hn5UtzPgYMqnM9LgP/MlwnVlP5JshhJFyqTX3+cbCZ3Flkwv8uAzwMXps6nVX/MbJxsMP23XOSwmSxQXR34vKRLyEI4fDgfHFO8i8x2dLGkS/NjgI8Ax+b1HsRds7DDgF/n1/IY4EN5+sa8jhAcBEuGCKkQVEbSKjPblv9S/0/gCjP74AL15TzgjWa2aQ51TJ/PHmSzvkPbDcBLiVxVuMHMblnovgRBipj5BDPh5fnM4lKypaGPL2BfbiOze5Q2mc6Ab+Tn8xPgXcth4FG+yZRsRjW1wN0JgpbEzCcIeoyklwDHu+T/MbNXp/IHwa5ADD5BEATBvBPLbkEQBMG8E4NPEARBMO/E4BMEQRDMOzH4BEEQBPPO/wNavBTdAoHMAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting and plotting the data in one line\n",
    "ds1.tas.isel(time=0).plot(); # add semicolon to suppress text output, depends on individual taste\n",
    "plt.title('My plot');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:58:14.695178Z",
     "start_time": "2019-06-21T13:58:06.805297Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting lat, lon values\n",
    "# quick time series plot for Bern\n",
    "ds1.tas.sel(lat=46.9, lon=7.4, method='nearest').plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:57:58.151775Z",
     "start_time": "2019-06-21T13:57:58.126576Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --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",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\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, 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;tas&#x27; (time: 4, lat: 41, lon: 61)&gt;\n",
       "array([[[290.75778, 289.63876, ..., 287.49075, 287.80527],\n",
       "        [290.72467, 289.38992, ..., 285.9566 , 286.85046],\n",
       "        ...,\n",
       "        [283.37347, 283.232  , ..., 287.06198, 287.55432],\n",
       "        [283.63815, 283.44315, ..., 287.76877, 288.1402 ]],\n",
       "\n",
       "       [[291.91455, 290.77673, ..., 289.2526 , 289.43515],\n",
       "        [291.76596, 290.4617 , ..., 287.54364, 288.15375],\n",
       "        ...,\n",
       "        [287.03748, 287.13834, ..., 288.28333, 288.5485 ],\n",
       "        [287.1493 , 287.20032, ..., 288.73782, 288.91595]],\n",
       "\n",
       "       [[289.4247 , 288.60004, ..., 289.39343, 289.64795],\n",
       "        [289.21817, 288.27322, ..., 288.27432, 288.9979 ],\n",
       "        ...,\n",
       "        [289.7949 , 289.67392, ..., 288.4745 , 288.6663 ],\n",
       "        [290.14218, 290.0029 , ..., 288.83496, 288.94214]],\n",
       "\n",
       "       [[291.67188, 290.38046, ..., 288.0555 , 288.266  ],\n",
       "        [291.76273, 290.2959 , ..., 286.43536, 287.28995],\n",
       "        ...,\n",
       "        [287.30673, 287.2753 , ..., 288.64294, 288.8205 ],\n",
       "        [287.46466, 287.4314 , ..., 289.00394, 289.0991 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2071-05-15T12:00:00 ... 2071-05-18T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-baf3bc85-d788-4ed1-b464-9fe210b511e5' class='xr-array-in' type='checkbox' checked><label for='section-baf3bc85-d788-4ed1-b464-9fe210b511e5' 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>290.75778 289.63876 288.34048 ... 289.0715 289.00394 289.0991</span></div><div class='xr-array-data'><pre>array([[[290.75778, 289.63876, ..., 287.49075, 287.80527],\n",
       "        [290.72467, 289.38992, ..., 285.9566 , 286.85046],\n",
       "        ...,\n",
       "        [283.37347, 283.232  , ..., 287.06198, 287.55432],\n",
       "        [283.63815, 283.44315, ..., 287.76877, 288.1402 ]],\n",
       "\n",
       "       [[291.91455, 290.77673, ..., 289.2526 , 289.43515],\n",
       "        [291.76596, 290.4617 , ..., 287.54364, 288.15375],\n",
       "        ...,\n",
       "        [287.03748, 287.13834, ..., 288.28333, 288.5485 ],\n",
       "        [287.1493 , 287.20032, ..., 288.73782, 288.91595]],\n",
       "\n",
       "       [[289.4247 , 288.60004, ..., 289.39343, 289.64795],\n",
       "        [289.21817, 288.27322, ..., 288.27432, 288.9979 ],\n",
       "        ...,\n",
       "        [289.7949 , 289.67392, ..., 288.4745 , 288.6663 ],\n",
       "        [290.14218, 290.0029 , ..., 288.83496, 288.94214]],\n",
       "\n",
       "       [[291.67188, 290.38046, ..., 288.0555 , 288.266  ],\n",
       "        [291.76273, 290.2959 , ..., 286.43536, 287.28995],\n",
       "        ...,\n",
       "        [287.30673, 287.2753 , ..., 288.64294, 288.8205 ],\n",
       "        [287.46466, 287.4314 , ..., 289.00394, 289.0991 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e6c455f2-fb33-4786-b309-1f5d19ee3ca0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6c455f2-fb33-4786-b309-1f5d19ee3ca0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-05-15T12:00:00 ... 2071-05-...</div><input id='attrs-8a13375f-111f-4f27-8a98-f181715e4686' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a13375f-111f-4f27-8a98-f181715e4686' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1576094e-7527-413e-9363-5ba870d5b94f' class='xr-var-data-in' type='checkbox'><label for='data-1576094e-7527-413e-9363-5ba870d5b94f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2071-05-15T12:00:00.000000000&#x27;, &#x27;2071-05-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2071-05-17T12:00:00.000000000&#x27;, &#x27;2071-05-18T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.1 5.2 5.3 ... 10.8 10.9 11.0</div><input id='attrs-e7cde89a-eaaf-4999-8a58-954fc03b2992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7cde89a-eaaf-4999-8a58-954fc03b2992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ecffbf0-4f7b-439e-89de-09a5641f43f3' class='xr-var-data-in' type='checkbox'><label for='data-4ecffbf0-4f7b-439e-89de-09a5641f43f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,\n",
       "        6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,\n",
       "        7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,\n",
       "        8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.1 44.2 ... 47.8 47.9 48.0</div><input id='attrs-e1fb7788-bb8c-482c-962f-307065fa5445' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1fb7788-bb8c-482c-962f-307065fa5445' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cad15db-95b1-498a-822d-38960a983342' class='xr-var-data-in' type='checkbox'><label for='data-7cad15db-95b1-498a-822d-38960a983342' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,\n",
       "       45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3,\n",
       "       46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5,\n",
       "       47.6, 47.7, 47.8, 47.9, 48. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2813e7c-77e0-42e7-9c41-bfcbe8220730' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2813e7c-77e0-42e7-9c41-bfcbe8220730' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 4, lat: 41, lon: 61)>\n",
       "array([[[290.75778, 289.63876, ..., 287.49075, 287.80527],\n",
       "        [290.72467, 289.38992, ..., 285.9566 , 286.85046],\n",
       "        ...,\n",
       "        [283.37347, 283.232  , ..., 287.06198, 287.55432],\n",
       "        [283.63815, 283.44315, ..., 287.76877, 288.1402 ]],\n",
       "\n",
       "       [[291.91455, 290.77673, ..., 289.2526 , 289.43515],\n",
       "        [291.76596, 290.4617 , ..., 287.54364, 288.15375],\n",
       "        ...,\n",
       "        [287.03748, 287.13834, ..., 288.28333, 288.5485 ],\n",
       "        [287.1493 , 287.20032, ..., 288.73782, 288.91595]],\n",
       "\n",
       "       [[289.4247 , 288.60004, ..., 289.39343, 289.64795],\n",
       "        [289.21817, 288.27322, ..., 288.27432, 288.9979 ],\n",
       "        ...,\n",
       "        [289.7949 , 289.67392, ..., 288.4745 , 288.6663 ],\n",
       "        [290.14218, 290.0029 , ..., 288.83496, 288.94214]],\n",
       "\n",
       "       [[291.67188, 290.38046, ..., 288.0555 , 288.266  ],\n",
       "        [291.76273, 290.2959 , ..., 286.43536, 287.28995],\n",
       "        ...,\n",
       "        [287.30673, 287.2753 , ..., 288.64294, 288.8205 ],\n",
       "        [287.46466, 287.4314 , ..., 289.00394, 289.0991 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2071-05-15T12:00:00 ... 2071-05-18T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## first we select data to plot\n",
    "## we can select multiple time-steps using slice function\n",
    "temp = ds1.tas.sel(time=slice('2071-05-15', '2071-05-18' ))\n",
    "temp # print preview of our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Since, we have 4 time steps, we can plot it in a _2x2_ fashion__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-21T13:58:01.710255Z",
     "start_time": "2019-06-21T13:57:58.154578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 504x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot(col='time',col_wrap=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calling-basic numpy functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; ()&gt;\n",
       "array(236.40562439)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d252704-7b78-41ec-8f78-e655877268fd' class='xr-array-in' type='checkbox' checked><label for='section-4d252704-7b78-41ec-8f78-e655877268fd' 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>236.4</span></div><div class='xr-array-data'><pre>array(236.40562439)</pre></div></div></li><li class='xr-section-item'><input id='section-d13da211-32ab-47d9-8a25-99f3ae72bd44' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d13da211-32ab-47d9-8a25-99f3ae72bd44' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-42c21778-8560-48a1-98f4-9e1a7b47f60c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-42c21778-8560-48a1-98f4-9e1a7b47f60c' 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 'tas' ()>\n",
       "array(236.40562439)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; ()&gt;\n",
       "array(310.14517212)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6bb10358-5913-48f4-ba06-4c52846d9780' class='xr-array-in' type='checkbox' checked><label for='section-6bb10358-5913-48f4-ba06-4c52846d9780' 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>310.1</span></div><div class='xr-array-data'><pre>array(310.14517212)</pre></div></div></li><li class='xr-section-item'><input id='section-fa515198-3f2a-466f-a583-3e6e2b67a1cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa515198-3f2a-466f-a583-3e6e2b67a1cf' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0d7c55af-8b2b-497f-b712-10b36045d9f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d7c55af-8b2b-497f-b712-10b36045d9f2' 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 'tas' ()>\n",
       "array(310.14517212)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "array([[284.12195, 284.39084, 284.62885, ..., 272.45322, 271.99628,\n",
       "        271.81552],\n",
       "       [283.71564, 283.8571 , 284.33527, ..., 272.58676, 272.29962,\n",
       "        272.0547 ],\n",
       "       [283.5277 , 283.67834, 284.0796 , ..., 272.68976, 272.3679 ,\n",
       "        271.9453 ],\n",
       "       ...,\n",
       "       [262.7695 , 262.81473, 262.86206, ..., 256.67145, 256.40466,\n",
       "        256.13693],\n",
       "       [262.5683 , 262.61404, 262.66064, ..., 256.7328 , 256.46802,\n",
       "        256.19617],\n",
       "       [262.36996, 262.4136 , 262.45786, ..., 256.77982, 256.52728,\n",
       "        256.25998]], dtype=float32)\n",
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       "  * lon      (lon) float64 -10.0 -9.9 -9.8 -9.7 -9.6 ... 36.7 36.8 36.9 37.0\n",
       "  * lat      (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 70.4 70.5 70.6 70.7 70.8</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 409</li><li><span class='xr-has-index'>lon</span>: 471</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6f47c3ae-6278-4e60-8574-a327943f3625' class='xr-array-in' type='checkbox' checked><label for='section-6f47c3ae-6278-4e60-8574-a327943f3625' 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>284.12195 284.39084 284.62885 ... 256.77982 256.52728 256.25998</span></div><div class='xr-array-data'><pre>array([[284.12195, 284.39084, 284.62885, ..., 272.45322, 271.99628,\n",
       "        271.81552],\n",
       "       [283.71564, 283.8571 , 284.33527, ..., 272.58676, 272.29962,\n",
       "        272.0547 ],\n",
       "       [283.5277 , 283.67834, 284.0796 , ..., 272.68976, 272.3679 ,\n",
       "        271.9453 ],\n",
       "       ...,\n",
       "       [262.7695 , 262.81473, 262.86206, ..., 256.67145, 256.40466,\n",
       "        256.13693],\n",
       "       [262.5683 , 262.61404, 262.66064, ..., 256.7328 , 256.46802,\n",
       "        256.19617],\n",
       "       [262.36996, 262.4136 , 262.45786, ..., 256.77982, 256.52728,\n",
       "        256.25998]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-622f53a8-7566-42f4-9a67-e20347d2e016' class='xr-section-summary-in' type='checkbox'  checked><label for='section-622f53a8-7566-42f4-9a67-e20347d2e016' 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'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.9 -9.8 ... 36.8 36.9 37.0</div><input id='attrs-5da80561-3e3b-4b45-8333-413d1e4a1be4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5da80561-3e3b-4b45-8333-413d1e4a1be4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa045d7-83e4-4ae5-83cd-9fdc2132aa16' class='xr-var-data-in' type='checkbox'><label for='data-daa045d7-83e4-4ae5-83cd-9fdc2132aa16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-10. ,  -9.9,  -9.8, ...,  36.8,  36.9,  37. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.0 30.1 30.2 ... 70.6 70.7 70.8</div><input id='attrs-5e530f7c-1567-4bd1-a1a5-08368e7f8d94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e530f7c-1567-4bd1-a1a5-08368e7f8d94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b062eb30-30a0-4e30-a13b-0f1e7134af59' class='xr-var-data-in' type='checkbox'><label for='data-b062eb30-30a0-4e30-a13b-0f1e7134af59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([30. , 30.1, 30.2, ..., 70.6, 70.7, 70.8])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62d7b17f-3a3f-471e-8b65-9eecf429bb05' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62d7b17f-3a3f-471e-8b65-9eecf429bb05' 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 'tas' (lat: 409, lon: 471)>\n",
       "array([[284.12195, 284.39084, 284.62885, ..., 272.45322, 271.99628,\n",
       "        271.81552],\n",
       "       [283.71564, 283.8571 , 284.33527, ..., 272.58676, 272.29962,\n",
       "        272.0547 ],\n",
       "       [283.5277 , 283.67834, 284.0796 , ..., 272.68976, 272.3679 ,\n",
       "        271.9453 ],\n",
       "       ...,\n",
       "       [262.7695 , 262.81473, 262.86206, ..., 256.67145, 256.40466,\n",
       "        256.13693],\n",
       "       [262.5683 , 262.61404, 262.66064, ..., 256.7328 , 256.46802,\n",
       "        256.19617],\n",
       "       [262.36996, 262.4136 , 262.45786, ..., 256.77982, 256.52728,\n",
       "        256.25998]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -10.0 -9.9 -9.8 -9.7 -9.6 ... 36.7 36.8 36.9 37.0\n",
       "  * lat      (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 70.4 70.5 70.6 70.7 70.8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applying along a specific dimension\n",
    "ds1.tas.min(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7192726ed0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds1.tas.min(dim='time').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**These numpy functions are also now directly supported in the latest xarray version**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tas&#x27; ()&gt;\n",
       "array(236.40562439)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f50957ff-a1ab-42a7-8ecf-2b91847356cd' class='xr-array-in' type='checkbox' checked><label for='section-f50957ff-a1ab-42a7-8ecf-2b91847356cd' 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>236.4</span></div><div class='xr-array-data'><pre>array(236.40562439)</pre></div></div></li><li class='xr-section-item'><input id='section-faea0d2b-286f-43d7-b001-2a5593a66d02' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-faea0d2b-286f-43d7-b001-2a5593a66d02' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-909927a4-6116-419a-b065-fd07378aa620' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-909927a4-6116-419a-b065-fd07378aa620' 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 'tas' ()>\n",
       "array(236.40562439)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(ds1.tas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modyfying values inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing into deg Celsius\n",
    "ds1.tas.values = ds1.tas.values - 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be careful when modifying data like that because the metadata says temperature is in Kelvin. So let's correct the meta data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'standard_name': 'air_temperature',\n",
       " 'long_name': 'Near-Surface Air Temperature',\n",
       " 'units': 'K',\n",
       " 'cell_methods': 'time: mean'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.attrs"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1.tas.attrs['units'] = 'degC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'standard_name': 'air_temperature',\n",
       " 'long_name': 'Near-Surface Air Temperature',\n",
       " 'units': 'degC',\n",
       " 'cell_methods': 'time: mean'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.tas.attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       ".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, 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;tas&#x27; (time: 1826, lat: 41, lon: 61)&gt;\n",
       "array([[[ 9.210602 ,  8.772339 ,  8.238678 , ...,  8.18219  ,\n",
       "          8.291138 ,  8.668549 ],\n",
       "        [ 9.027435 ,  8.46106  ,  7.6687927, ...,  6.874939 ,\n",
       "          7.2055054,  7.722046 ],\n",
       "        [ 8.613464 ,  8.027374 ,  6.99704  , ...,  7.1250916,\n",
       "          7.638214 ,  8.143005 ],\n",
       "        ...,\n",
       "        [ 6.494232 ,  6.3919983,  6.3206787, ...,  6.625458 ,\n",
       "          6.665497 ,  6.715271 ],\n",
       "        [ 6.6833496,  6.5343018,  6.4110413, ...,  5.9276123,\n",
       "          5.865326 ,  5.786438 ],\n",
       "        [ 6.8246155,  6.672882 ,  6.5183105, ...,  5.2502136,\n",
       "          5.1925964,  5.0598755]],\n",
       "\n",
       "       [[ 7.9325867,  7.0742188,  6.164551 , ...,  6.080261 ,\n",
       "          6.1978455,  6.503113 ],\n",
       "        [ 7.9779053,  7.115753 ,  6.2164   , ...,  4.5149536,\n",
       "          4.940613 ,  5.5752563],\n",
       "        [ 7.9615784,  7.351654 ,  6.5687256, ...,  4.8573914,\n",
       "          5.5573425,  6.255829 ],\n",
       "...\n",
       "        [10.543854 , 10.430054 , 10.3845825, ...,  6.368622 ,\n",
       "          6.608032 ,  6.884491 ],\n",
       "        [10.840027 , 10.658905 , 10.532379 , ...,  6.657318 ,\n",
       "          6.503998 ,  6.3548584],\n",
       "        [11.079651 , 10.870209 , 10.676178 , ...,  6.6805115,\n",
       "          6.2316895,  5.721466 ]],\n",
       "\n",
       "       [[15.100769 , 14.230743 , 13.112732 , ...,  8.618774 ,\n",
       "          8.561432 ,  8.843597 ],\n",
       "        [14.8281555, 13.879791 , 12.629425 , ...,  8.248962 ,\n",
       "          8.812225 ,  9.38559  ],\n",
       "        [14.32019  , 13.498291 , 12.191223 , ...,  8.873688 ,\n",
       "          9.684357 , 10.433197 ],\n",
       "        ...,\n",
       "        [10.873749 , 10.83429  , 10.838501 , ..., 10.590546 ,\n",
       "         10.674408 , 10.808411 ],\n",
       "        [10.995941 , 10.919189 , 10.899567 , ..., 10.755127 ,\n",
       "         10.5321045, 10.286133 ],\n",
       "        [11.060211 , 10.993225 , 10.941132 , ..., 10.927948 ,\n",
       "         10.475983 ,  9.888367 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2071-01-01T12:00:00 ... 2075-12-31T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          degC\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1826</li><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a16a4554-4ded-4056-8176-01226b71ff58' class='xr-array-in' type='checkbox' checked><label for='section-a16a4554-4ded-4056-8176-01226b71ff58' 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>9.210602 8.772339 8.238678 7.6837463 ... 10.927948 10.475983 9.888367</span></div><div class='xr-array-data'><pre>array([[[ 9.210602 ,  8.772339 ,  8.238678 , ...,  8.18219  ,\n",
       "          8.291138 ,  8.668549 ],\n",
       "        [ 9.027435 ,  8.46106  ,  7.6687927, ...,  6.874939 ,\n",
       "          7.2055054,  7.722046 ],\n",
       "        [ 8.613464 ,  8.027374 ,  6.99704  , ...,  7.1250916,\n",
       "          7.638214 ,  8.143005 ],\n",
       "        ...,\n",
       "        [ 6.494232 ,  6.3919983,  6.3206787, ...,  6.625458 ,\n",
       "          6.665497 ,  6.715271 ],\n",
       "        [ 6.6833496,  6.5343018,  6.4110413, ...,  5.9276123,\n",
       "          5.865326 ,  5.786438 ],\n",
       "        [ 6.8246155,  6.672882 ,  6.5183105, ...,  5.2502136,\n",
       "          5.1925964,  5.0598755]],\n",
       "\n",
       "       [[ 7.9325867,  7.0742188,  6.164551 , ...,  6.080261 ,\n",
       "          6.1978455,  6.503113 ],\n",
       "        [ 7.9779053,  7.115753 ,  6.2164   , ...,  4.5149536,\n",
       "          4.940613 ,  5.5752563],\n",
       "        [ 7.9615784,  7.351654 ,  6.5687256, ...,  4.8573914,\n",
       "          5.5573425,  6.255829 ],\n",
       "...\n",
       "        [10.543854 , 10.430054 , 10.3845825, ...,  6.368622 ,\n",
       "          6.608032 ,  6.884491 ],\n",
       "        [10.840027 , 10.658905 , 10.532379 , ...,  6.657318 ,\n",
       "          6.503998 ,  6.3548584],\n",
       "        [11.079651 , 10.870209 , 10.676178 , ...,  6.6805115,\n",
       "          6.2316895,  5.721466 ]],\n",
       "\n",
       "       [[15.100769 , 14.230743 , 13.112732 , ...,  8.618774 ,\n",
       "          8.561432 ,  8.843597 ],\n",
       "        [14.8281555, 13.879791 , 12.629425 , ...,  8.248962 ,\n",
       "          8.812225 ,  9.38559  ],\n",
       "        [14.32019  , 13.498291 , 12.191223 , ...,  8.873688 ,\n",
       "          9.684357 , 10.433197 ],\n",
       "        ...,\n",
       "        [10.873749 , 10.83429  , 10.838501 , ..., 10.590546 ,\n",
       "         10.674408 , 10.808411 ],\n",
       "        [10.995941 , 10.919189 , 10.899567 , ..., 10.755127 ,\n",
       "         10.5321045, 10.286133 ],\n",
       "        [11.060211 , 10.993225 , 10.941132 , ..., 10.927948 ,\n",
       "         10.475983 ,  9.888367 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a37c78c1-65ed-469b-8b46-b7773aa90ffa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a37c78c1-65ed-469b-8b46-b7773aa90ffa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-01-01T12:00:00 ... 2075-12-...</div><input id='attrs-fd1ffafe-68c5-4402-b629-028d1ab5b7ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd1ffafe-68c5-4402-b629-028d1ab5b7ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43f7a78b-c9f5-452b-87f8-9f4f02160572' class='xr-var-data-in' type='checkbox'><label for='data-43f7a78b-c9f5-452b-87f8-9f4f02160572' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2071-01-01T12:00:00.000000000&#x27;, &#x27;2071-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2071-01-03T12:00:00.000000000&#x27;, ..., &#x27;2075-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2075-12-30T12:00:00.000000000&#x27;, &#x27;2075-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.1 5.2 5.3 ... 10.8 10.9 11.0</div><input id='attrs-148d60e8-e32a-42ac-b3ca-90b1028f813c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-148d60e8-e32a-42ac-b3ca-90b1028f813c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da34f9ea-a2e5-466f-9e8b-aee5e3354b18' class='xr-var-data-in' type='checkbox'><label for='data-da34f9ea-a2e5-466f-9e8b-aee5e3354b18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,\n",
       "        6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,\n",
       "        7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,\n",
       "        8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.1 44.2 ... 47.8 47.9 48.0</div><input id='attrs-28938038-a70a-4900-a1ff-874120c8ca95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28938038-a70a-4900-a1ff-874120c8ca95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47e9dae8-85f1-4928-8030-b71061452daf' class='xr-var-data-in' type='checkbox'><label for='data-47e9dae8-85f1-4928-8030-b71061452daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,\n",
       "       45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3,\n",
       "       46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5,\n",
       "       47.6, 47.7, 47.8, 47.9, 48. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec6895a4-5b23-4630-acee-fd377874c9a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec6895a4-5b23-4630-acee-fd377874c9a6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 1826, lat: 41, lon: 61)>\n",
       "array([[[ 9.210602 ,  8.772339 ,  8.238678 , ...,  8.18219  ,\n",
       "          8.291138 ,  8.668549 ],\n",
       "        [ 9.027435 ,  8.46106  ,  7.6687927, ...,  6.874939 ,\n",
       "          7.2055054,  7.722046 ],\n",
       "        [ 8.613464 ,  8.027374 ,  6.99704  , ...,  7.1250916,\n",
       "          7.638214 ,  8.143005 ],\n",
       "        ...,\n",
       "        [ 6.494232 ,  6.3919983,  6.3206787, ...,  6.625458 ,\n",
       "          6.665497 ,  6.715271 ],\n",
       "        [ 6.6833496,  6.5343018,  6.4110413, ...,  5.9276123,\n",
       "          5.865326 ,  5.786438 ],\n",
       "        [ 6.8246155,  6.672882 ,  6.5183105, ...,  5.2502136,\n",
       "          5.1925964,  5.0598755]],\n",
       "\n",
       "       [[ 7.9325867,  7.0742188,  6.164551 , ...,  6.080261 ,\n",
       "          6.1978455,  6.503113 ],\n",
       "        [ 7.9779053,  7.115753 ,  6.2164   , ...,  4.5149536,\n",
       "          4.940613 ,  5.5752563],\n",
       "        [ 7.9615784,  7.351654 ,  6.5687256, ...,  4.8573914,\n",
       "          5.5573425,  6.255829 ],\n",
       "...\n",
       "        [10.543854 , 10.430054 , 10.3845825, ...,  6.368622 ,\n",
       "          6.608032 ,  6.884491 ],\n",
       "        [10.840027 , 10.658905 , 10.532379 , ...,  6.657318 ,\n",
       "          6.503998 ,  6.3548584],\n",
       "        [11.079651 , 10.870209 , 10.676178 , ...,  6.6805115,\n",
       "          6.2316895,  5.721466 ]],\n",
       "\n",
       "       [[15.100769 , 14.230743 , 13.112732 , ...,  8.618774 ,\n",
       "          8.561432 ,  8.843597 ],\n",
       "        [14.8281555, 13.879791 , 12.629425 , ...,  8.248962 ,\n",
       "          8.812225 ,  9.38559  ],\n",
       "        [14.32019  , 13.498291 , 12.191223 , ...,  8.873688 ,\n",
       "          9.684357 , 10.433197 ],\n",
       "        ...,\n",
       "        [10.873749 , 10.83429  , 10.838501 , ..., 10.590546 ,\n",
       "         10.674408 , 10.808411 ],\n",
       "        [10.995941 , 10.919189 , 10.899567 , ..., 10.755127 ,\n",
       "         10.5321045, 10.286133 ],\n",
       "        [11.060211 , 10.993225 , 10.941132 , ..., 10.927948 ,\n",
       "         10.475983 ,  9.888367 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2071-01-01T12:00:00 ... 2075-12-31T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          degC\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check again\n",
    "ds1.tas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To avoid confusion save it as a separate variable when you modify things inplace\n",
    "\n",
    "tas_in_degC = ds1.tas - 273.15\n",
    "\n",
    "# now the new variable is just an xarray dataarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xarray.core.dataarray.DataArray"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now the new variable is just an xarray dataarray\n",
    "type(tas_in_degC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xarray.core.dataset.Dataset"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ds1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving file to disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have your `data_to_plot` file, it is better to save the file to disk especially if the calculations to produce the file took a while.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  padding-right: 10px;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "}\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;tas&#x27; (lat: 41, lon: 61)&gt;\n",
       "array([[290.75778, 289.63876, 288.34048, ..., 287.10742, 287.49075, 287.80527],\n",
       "       [290.72467, 289.38992, 287.91345, ..., 285.26715, 285.9566 , 286.85046],\n",
       "       [290.2865 , 288.90448, 287.25934, ..., 285.4207 , 286.54886, 287.682  ],\n",
       "       ...,\n",
       "       [283.21   , 283.16342, 283.18152, ..., 286.4937 , 286.8491 , 287.3354 ],\n",
       "       [283.37347, 283.232  , 283.19907, ..., 286.67203, 287.06198, 287.55432],\n",
       "       [283.63815, 283.44315, 283.32687, ..., 287.4437 , 287.76877, 288.1402 ]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-05-15T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 41</li><li><span class='xr-has-index'>lon</span>: 61</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-48ee343d-1e9b-4ea6-99a5-b9ff370cb77b' class='xr-array-in' type='checkbox' checked><label for='section-48ee343d-1e9b-4ea6-99a5-b9ff370cb77b' 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>290.75778 289.63876 288.34048 ... 287.4437 287.76877 288.1402</span></div><div class='xr-array-data'><pre>array([[290.75778, 289.63876, 288.34048, ..., 287.10742, 287.49075, 287.80527],\n",
       "       [290.72467, 289.38992, 287.91345, ..., 285.26715, 285.9566 , 286.85046],\n",
       "       [290.2865 , 288.90448, 287.25934, ..., 285.4207 , 286.54886, 287.682  ],\n",
       "       ...,\n",
       "       [283.21   , 283.16342, 283.18152, ..., 286.4937 , 286.8491 , 287.3354 ],\n",
       "       [283.37347, 283.232  , 283.19907, ..., 286.67203, 287.06198, 287.55432],\n",
       "       [283.63815, 283.44315, 283.32687, ..., 287.4437 , 287.76877, 288.1402 ]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-7afb2b73-432c-45d9-ac48-db12dd888faa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7afb2b73-432c-45d9-ac48-db12dd888faa' 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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2071-05-15T12:00:00</div><input id='attrs-ae0c879f-ecd7-453c-9b99-e933597ac07f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae0c879f-ecd7-453c-9b99-e933597ac07f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b70f46d1-d368-43b1-8680-b73ae11e85bb' class='xr-var-data-in' type='checkbox'><label for='data-b70f46d1-d368-43b1-8680-b73ae11e85bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2071-05-15T12:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 5.1 5.2 5.3 ... 10.8 10.9 11.0</div><input id='attrs-fc7294de-93ea-449f-931f-7ca729d1c7a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc7294de-93ea-449f-931f-7ca729d1c7a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d1befd1-4c51-4840-9a42-dea601964013' class='xr-var-data-in' type='checkbox'><label for='data-1d1befd1-4c51-4840-9a42-dea601964013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,\n",
       "        6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,\n",
       "        7.4,  7.5,  7.6,  7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,\n",
       "        8.6,  8.7,  8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,\n",
       "        9.8,  9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,\n",
       "       11. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.0 44.1 44.2 ... 47.8 47.9 48.0</div><input id='attrs-d1f02948-ceff-41b9-98b3-cbd2b624cd4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1f02948-ceff-41b9-98b3-cbd2b624cd4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a679fd81-2e46-44d1-9f83-b8be6ca2fb9c' class='xr-var-data-in' type='checkbox'><label for='data-a679fd81-2e46-44d1-9f83-b8be6ca2fb9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,\n",
       "       45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2, 46.3,\n",
       "       46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3, 47.4, 47.5,\n",
       "       47.6, 47.7, 47.8, 47.9, 48. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1274782f-44d3-4ffd-ad31-5821357c5d09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1274782f-44d3-4ffd-ad31-5821357c5d09' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (lat: 41, lon: 61)>\n",
       "array([[290.75778, 289.63876, 288.34048, ..., 287.10742, 287.49075, 287.80527],\n",
       "       [290.72467, 289.38992, 287.91345, ..., 285.26715, 285.9566 , 286.85046],\n",
       "       [290.2865 , 288.90448, 287.25934, ..., 285.4207 , 286.54886, 287.682  ],\n",
       "       ...,\n",
       "       [283.21   , 283.16342, 283.18152, ..., 286.4937 , 286.8491 , 287.3354 ],\n",
       "       [283.37347, 283.232  , 283.19907, ..., 286.67203, 287.06198, 287.55432],\n",
       "       [283.63815, 283.44315, 283.32687, ..., 287.4437 , 287.76877, 288.1402 ]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2071-05-15T12:00:00\n",
       "  * lon      (lon) float64 5.0 5.1 5.2 5.3 5.4 5.5 ... 10.6 10.7 10.8 10.9 11.0\n",
       "  * lat      (lat) float64 44.0 44.1 44.2 44.3 44.4 ... 47.6 47.7 47.8 47.9 48.0\n",
       "Attributes:\n",
       "    standard_name:  air_temperature\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    units:          K\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_file = '/home/mali/workdir/data_to_plot.nc' # change it to your directory\n",
    "data_to_plot.to_netcdf(out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common Error Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ds' 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<ipython-input-7-6f4a4004dcca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'ds' is not defined"
     ]
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1.tas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'2000-01-01'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 946684800000000000",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2894\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2895\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   2896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.DatetimeEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: Timestamp('2000-01-01 00:00:00')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    624\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\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    626\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: Timestamp('2000-01-01 00:00:00')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-100-ede4b8d0407c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2000-01-01'\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/usr/local/lib/python3.7/dist-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36msel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   2104\u001b[0m         \u001b[0mindexers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meither_dict_or_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m         pos_indexers, new_indexes = remap_label_indexers(\n\u001b[0;32m-> 2106\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2107\u001b[0m         )\n\u001b[1;32m   2108\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xarray/core/coordinates.py\u001b[0m in \u001b[0;36mremap_label_indexers\u001b[0;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m     pos_indexers, new_indexes = indexing.remap_label_indexers(\n\u001b[0;32m--> 398\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m     )\n\u001b[1;32m    400\u001b[0m     \u001b[0;31m# attach indexer's coordinate to pos_indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36mremap_label_indexers\u001b[0;34m(data_obj, indexers, method, tolerance)\u001b[0m\n\u001b[1;32m    273\u001b[0m             \u001b[0mcoords_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_cast_to_coords_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0midxr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_label_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\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    276\u001b[0m             \u001b[0mpos_indexers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midxr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnew_idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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;32m/usr/local/lib/python3.7/dist-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36mconvert_label_indexer\u001b[0;34m(index, label, index_name, method, tolerance)\u001b[0m\n\u001b[1;32m    194\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\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    197\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    625\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_key\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_maybe_cast_for_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTimestamp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '2000-01-01'"
     ]
    }
   ],
   "source": [
    "ds1.sel(time='2000-01-01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'cat'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-4ea48687a338>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cat\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'cat'"
     ]
    }
   ],
   "source": [
    "int(\"cat\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Given file dataset contains more than one data variable. Please read with xarray.open_dataset and then select the variable you want.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-63b80e27acee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile1\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/usr/local/lib/python3.7/dist-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_dataarray\u001b[0;34m(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    690\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_vars\u001b[0m\u001b[0;34m)\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[0m\n\u001b[1;32m    691\u001b[0m         raise ValueError(\n\u001b[0;32m--> 692\u001b[0;31m             \u001b[0;34m\"Given file dataset contains more than one data \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    693\u001b[0m             \u001b[0;34m\"variable. Please read with xarray.open_dataset and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m             \u001b[0;34m\"then select the variable you want.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Given file dataset contains more than one data variable. Please read with xarray.open_dataset and then select the variable you want."
     ]
    }
   ],
   "source": [
    "xr.open_dataarray(file1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type Error\n",
    "\n",
    "Common cause can be calling an object which is not a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'DataArray' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-f344cebaa475>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtas\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[0;31mTypeError\u001b[0m: 'DataArray' object is not callable"
     ]
    }
   ],
   "source": [
    "ds1.tas()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Show slicing error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with Errors\n",
    "\n",
    "* Asking Google for help\n",
    "* Looking up on stackoverflow for FAQs\n",
    "\n",
    "For example, Google this: `How to change the range of the x-axis and y-axis python?`\n",
    "\n",
    "You will most likely see the following [stackoverflow answer](https://stackoverflow.com/questions/29337123/how-to-change-the-range-of-the-x-axis-and-y-axis-in-matlibplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Closing and deleting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1.close()\n",
    "del ds1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will only delete ds1 variable, it won't delete the original data stroed on disk. Note: Xarray never modifies original data on the disk in all the operations we looked above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References & Further Reading\n",
    "[xarray](http://xarray.pydata.org/)\n",
    "\n",
    "[Python Data Science Handbook - Visualization](https://www.oreilly.com/library/view/python-data-science/9781491912126/ch04.html)"
   ]
  }
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