docs/4_DownloadLandsatFromGEE.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![UCL](https://github.com/profLewis/Geog2021_Coursework/blob/master/images/ucl_logo.png?raw=true)\n",
    "\n",
    "# Download and visualise data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, you will learn how to download data using a web-based app developed using Google Earth Engine (GEE). GEE has access to a large number of Earth Observation and other datasets, and makes it easy to access these and do a some processing (at least for a limited spatial extent if you want high resolution data).\n",
    "\n",
    "You are provided with a web App built using GEE:\n",
    "\n",
    "This is available to you as a specific [App for the Shenzhen area](https://plewis.users.earthengine.app/view/shenzhen). The use of the App should be quite intuitive, but we will outline how to use it below.\n",
    "\n",
    "The app gives you access to 5 bands of Landsat surface reflectance data (see figure below), along with two vegetation indices, NDVI and NDWI. The datasets are annual composites, with each pixel the median of valid surface reflectance data for that year. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![loaded](images/loaded.jpeg)\n",
    "\n",
    "First load the App:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"490\"\n",
       "            src=\"https://plewis.users.earthengine.app/view/shenzhen\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x111e1fbe0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from IPython.display import IFrame\n",
    "IFrame('https://plewis.users.earthengine.app/view/shenzhen','100%',490)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, a composite dataset for the year 1986 is loaded, along with a rough initial classification.\n",
    "\n",
    "You can download these datasets from this interface by clicking on:\n",
    "\n",
    "* the [1986](http://www2.geog.ucl.ac.uk/~plewis/GEOG0027/1986/1986.zip) link to get the Landsat composite dataset\n",
    "* the [header](http://www2.geog.ucl.ac.uk/~plewis/GEOG0027/1986/1986_hdr.zip) link to get the associated `envi` header files\n",
    "\n",
    "You should download these files and *unzip* them into an appropriate directory in your Data directory (e.g. `~/Data/GEOG0027/1986`). You can also access the classification files  [class](http://www2.geog.ucl.ac.uk/~plewis/GEOG0027/1986/class1986.zip) but there is little point downloading these as we will not use them.\n",
    "\n",
    "You can change the year of the dataset in the right-hand panel. You can also load data for multiple years, although the App response may become slow (depending on network speeds). You can clear the loaded datasets with the `Clear ...` button.\n",
    "    \n",
    "For example (N.B. **once you have downloaded the files**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "# where the zip files are now, *after* having downloaded them\n",
    "in=./files\n",
    "# where we will put the files\n",
    "out=~/DATA/GEOG0027/\n",
    "\n",
    "mkdir -p $out\n",
    "unzip -u $in/1986.zip -d $out\n",
    "unzip -u $in/1986_hdr.zip -d $out\n",
    "mv $out/class1986/* $out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may find it more straightforward to use any tool prompted by your browser when downloading and extracting these files. In any case, make sure that *all* files for *all* archives go into a folder that is named after the year of the dataset (`1986` here). This is important for automatic further processing later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data archive\n",
    "\n",
    "Whilst you should find it instructive to download the datasets from the App, it is a little tedious to do that for all of the datasets.\n",
    "\n",
    "For this reason, on the UCL Geography system, you can access the entire dataset from a single file.\n",
    "\n",
    "To use this, open a Unix shell, and type the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "data=/home/plewis/public_html/GEOG0027/gee.tar.Z \n",
    "out=~/DATA/GEOG0027/\n",
    "\n",
    "mkdir -p $out\n",
    "cd $out\n",
    "tar xzf $data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are outside the UCL Geography system, you can access the archive file as a compressed `tar` archive from either of these links [[tar file](http://www2.geog.ucl.ac.uk/~plewis/GEOG0027/gee.tar.Z)  or [zip file](http://www2.geog.ucl.ac.uk/~plewis/GEOG0027/gee.zip)], but beware that the compressed file is `2.9G`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise time series\n",
    "\n",
    "You may have noticed a time series graph on the left-hand panel of the app. This appears when you click on any location in the image panel, and provides a visualisation of the time series of reflectance (and vegetation indices) for that location. You can pop the graph out to another browser tab (the grey square with arrow in the top left of the plot). \n",
    "\n",
    "\n",
    "It will look like this:\n",
    "\n",
    "![appgraph](images/appgraph.png)\n",
    "\n",
    "This is useful foir giving you an idea of the land cover history of this location, so you might want to produce graphs for several locations with different cover types, to use in your report.\n",
    "\n",
    "You can directly save the plot (`Download png` button) or save the dataset (`Download csv` button).\n",
    "\n",
    "If you download the `csv` file, you can then produce your own plots of the data (for your report).\n",
    "\n",
    "The example below are given in Python, but you should use whatever graphing package you are familiar with. Please make sure that (unlike below, which are for illustration only) you put full titles, labels and legends on graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system:time_start</th>\n",
       "      <th>B1_blue</th>\n",
       "      <th>B2_green</th>\n",
       "      <th>B3_red</th>\n",
       "      <th>B4_nir</th>\n",
       "      <th>B5_swir</th>\n",
       "      <th>B6_NDVI</th>\n",
       "      <th>B7_NDWI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986-06-06</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.187</td>\n",
       "      <td>-0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1987-06-06</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.136</td>\n",
       "      <td>-0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988-06-06</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989-06-06</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.154</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-06-06</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.097</td>\n",
       "      <td>-0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1991-06-06</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.105</td>\n",
       "      <td>-0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1992-06-06</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1993-06-06</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1994-06-06</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1995-06-06</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.109</td>\n",
       "      <td>-0.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1996-06-06</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1997-06-06</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.104</td>\n",
       "      <td>-0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1998-06-06</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1999-06-06</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-06-06</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2001-06-06</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2002-06-06</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2003-06-06</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2004-06-06</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.079</td>\n",
       "      <td>-0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2005-06-06</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.079</td>\n",
       "      <td>-0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2006-06-06</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.106</td>\n",
       "      <td>-0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2007-06-06</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2008-06-06</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2009-06-06</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010-06-06</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2011-06-06</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2012-06-06</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013-06-06</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-06-06</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.144</td>\n",
       "      <td>-0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.168</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2017-06-06</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.125</td>\n",
       "      <td>-0.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   system:time_start  B1_blue  B2_green  B3_red  B4_nir  B5_swir  B6_NDVI  \\\n",
       "0         1986-06-06    0.102     0.175   0.238   0.347    0.388    0.187   \n",
       "1         1987-06-06    0.103     0.177   0.227   0.299    0.412    0.136   \n",
       "2         1988-06-06    0.101     0.157   0.206   0.296    0.315    0.181   \n",
       "3         1989-06-06    0.104     0.158   0.192   0.262    0.295    0.154   \n",
       "4         1990-06-06    0.154     0.221   0.272   0.330    0.381    0.097   \n",
       "5         1991-06-06    0.133     0.187   0.211   0.260    0.300    0.105   \n",
       "6         1992-06-06    0.145     0.185   0.205   0.239    0.295    0.078   \n",
       "7         1993-06-06    0.144     0.188   0.205   0.249    0.293    0.098   \n",
       "8         1994-06-06    0.111     0.139   0.149   0.181    0.227    0.098   \n",
       "9         1995-06-06    0.114     0.136   0.137   0.170    0.213    0.109   \n",
       "10        1996-06-06    0.107     0.132   0.137   0.159    0.192    0.074   \n",
       "11        1997-06-06    0.098     0.121   0.132   0.162    0.190    0.104   \n",
       "12        1998-06-06    0.125     0.150   0.160   0.183    0.215    0.064   \n",
       "13        1999-06-06    0.123     0.152   0.167   0.183    0.200    0.045   \n",
       "14        2000-06-06    0.134     0.153   0.157   0.167    0.203    0.031   \n",
       "15        2001-06-06    0.110     0.131   0.140   0.152    0.166    0.038   \n",
       "16        2002-06-06    0.115     0.132   0.134   0.146    0.138    0.041   \n",
       "17        2003-06-06    0.113     0.128   0.138   0.153    0.152    0.054   \n",
       "18        2004-06-06    0.133     0.169   0.186   0.218    0.257    0.079   \n",
       "19        2005-06-06    0.140     0.181   0.199   0.233    0.257    0.079   \n",
       "20        2006-06-06    0.141     0.171   0.178   0.221    0.226    0.106   \n",
       "21        2007-06-06    0.141     0.186   0.205   0.242    0.257    0.084   \n",
       "22        2008-06-06    0.154     0.183   0.186   0.235    0.232    0.117   \n",
       "23        2009-06-06    0.145     0.170   0.180   0.211    0.217    0.078   \n",
       "24        2010-06-06    0.151     0.180   0.184   0.213    0.228    0.073   \n",
       "25        2011-06-06    0.144     0.169   0.175   0.202    0.227    0.072   \n",
       "26        2012-06-06    0.144     0.169   0.175   0.202    0.227    0.072   \n",
       "27        2013-06-06    0.127     0.156   0.165   0.216    0.215    0.135   \n",
       "28        2014-06-06    0.114     0.145   0.155   0.202    0.197    0.131   \n",
       "29        2015-06-06    0.128     0.158   0.169   0.226    0.240    0.144   \n",
       "30        2016-06-06    0.117     0.146   0.157   0.221    0.240    0.168   \n",
       "31        2017-06-06    0.123     0.153   0.161   0.209    0.209    0.129   \n",
       "32        2018-06-06    0.140     0.176   0.188   0.242    0.253    0.125   \n",
       "\n",
       "    B7_NDWI  \n",
       "0    -0.055  \n",
       "1    -0.159  \n",
       "2    -0.030  \n",
       "3    -0.058  \n",
       "4    -0.072  \n",
       "5    -0.072  \n",
       "6    -0.104  \n",
       "7    -0.081  \n",
       "8    -0.114  \n",
       "9    -0.111  \n",
       "10   -0.095  \n",
       "11   -0.079  \n",
       "12   -0.081  \n",
       "13   -0.045  \n",
       "14   -0.096  \n",
       "15   -0.045  \n",
       "16    0.027  \n",
       "17    0.004  \n",
       "18   -0.082  \n",
       "19   -0.050  \n",
       "20   -0.012  \n",
       "21   -0.029  \n",
       "22    0.007  \n",
       "23   -0.014  \n",
       "24   -0.035  \n",
       "25   -0.059  \n",
       "26   -0.059  \n",
       "27    0.003  \n",
       "28    0.013  \n",
       "29   -0.029  \n",
       "30   -0.040  \n",
       "31    0.001  \n",
       "32   -0.023  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d = pd.read_csv('files/ee-chart.csv')\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the time label for each dataset is `YYYY-06-06`. The day and month (`06-06`) have no significance here as this is an annual composite dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
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       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
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       "    this.waiting = false;\n",
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       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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       "            fig.send_message(\"refresh\", {});\n",
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       "\n",
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       "            if (fig.image_mode == 'full') {\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
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       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
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       "        event['data'] = 'scroll'\n",
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       "            event.step = -1;\n",
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       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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    "## Visualise spatial data\n",
    "\n",
    "You should be able to conveniently explore the spatial datasets in the GEE App. This is particularly useful for data interpretation (e.g. for training or validation) as you can have a map or high resolution image as a background.\n",
    "\n",
    "You can also load multiple years of data, and visualise the dynamics.\n",
    "\n",
    "Other than that, you will probably want to load the datasets into `envi` for visualisation. If you have correctly stored the data from the zip files, you should have in your data directory a series of sub-directories named by year, such as:\n",
    "\n",
    "    1986\t1989\t1992\t1995\t1998\t2001\t2004\t2007\t2010\t2013\t2016\t2019\n",
    "    1987\t1990\t1993\t1996\t1999\t2002\t2005\t2008\t2011\t2014\t2017\n",
    "    1988\t1991\t1994\t1997\t2000\t2003\t2006\t2009\t2012\t2015\t2018\n",
    "    \n",
    "Inside each of these, you should see files such as the following:\n",
    "\n",
    "    22014.constant.tif\t   2014.swir.tif\n",
    "    2014.NDVI.tfw\t\t\t2014.green.tfw\t\t\t\n",
    "    2014.NDVI.tif\t\t\t2014.green.tif\t\t\t\n",
    "    2014.NDWI.tfw\t\t\t2014.nir.tfw\t\t\t\n",
    "    2014.NDWI.tif\t\t\t2014.nir.tif\t\t\t\n",
    "    2014.blue.tfw\t\t\t2014.red.tfw\t\t\t\n",
    "    2014.blue.tif\t\t\t2014.red.tif\t\t\t\n",
    "    2014.constant.tfw\t\t2014.swir.tfw\n",
    "    2014_Shenzhen            2014_Shenzhen.hdr\n",
    "    \n",
    "The file `2014_Shenzhen` is particularly important, as this is the file that you will load using `envi`.\n",
    "\n",
    "![2014_load](images/2014_load.png)\n",
    "\n",
    "![2014_view](images/2014_view.png)\n",
    "\n",
    "One further file that may be of interest is the file `2014.constant.tif` which you can use as a data mask if you like. The mask has a value of zero for Shenzhen and `no data` outside of that.\n",
    "\n",
    "![mask_image](images/mask.png)\n",
    "\n"
   ]
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   "source": [
    "You should load and explore the datasets you have downloaded. Provide examples of e.g. interesting spectral profiles, transects, histograms or scatter plots for your report -- things that you would normally produce as part of a data exploration exercise.\n",
    "\n",
    "the histograms are of interest (think about what this means for classification)\n",
    "\n",
    "![histo2014](images/histo2014.png)\n",
    "\n",
    "and the scatterplots in many ways even more interesting:\n",
    "\n",
    "![2014scatter](images/2014scatter.png)\n",
    "\n",
    "\n",
    "You will find it particularly interesting to examine scatter plots of NDVI and NDWI for this dataset as you should see that a pretty good classification can be achieved with just these two bands of data. An advantage of this would be that the processing should be faster (fewer bands), but also that it is straightforward to visualise and appreciate the clusters that will be used to describe the land cover classes.\n",
    "\n",
    "These are not the only 'bands' to explore though! Collect plenty of images of scatterplotas, histograms and images for your write up to illustrate particular things you find. **Don't** just put images in the report with no reference to what you are using them to show!\n",
    "\n",
    "You should spend some time exploring these scatterplots for the different years data: are the patterns consistent? (show some evidence). Can you simply visualise (and explain) the clusters and their relationship to land cover types? In exploring the datasets you should save interesting plots to use in your write-up -- anything that helps you explain what is going on is of value!"
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