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<h1 id="Running-Facebook-Prophet-model-for-forecasting-Eurostat-tour_occ_nim-monthly-indicator">Running Facebook Prophet model for forecasting Eurostat <em>tour_occ_nim</em> monthly indicator<a class="anchor-link" href="#Running-Facebook-Prophet-model-for-forecasting-Eurostat-tour_occ_nim-monthly-indicator">¶</a></h1>
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<p>Facebook has just open sourced <a href="https://facebookincubator.github.io/prophet/"><code>Prophet</code></a>, a forecasting project available in <code>Python</code>.</p>
<p>At its core, the <code>Prophet</code> procedure is an <strong>additive regression model</strong> with four main components (based on <a href="http://mc-stan.org"><code>Stan</code></a> Bayesian approach):</p>
<ol>
<li>a piecewise linear (or logistic) growth curve trend: <code>Prophet</code> automatically detects changes in trends by selecting changepoints from the data,</li>
<li>a yearly seasonal component modeled using Fourier series,</li>
<li>a weekly seasonal component using dummy variables,</li>
<li>a user-provided list of important holidays.</li>
</ol>
<p>We will (<strong>blindly: we make no assumption whatsoever here</strong>, beyond the obvious seasonality of the data) use the features 1. and 2. of the model to <strong>build forecast estimates of Eurostat <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=tour_occ_nim&lang=en"><em>tour_occ_nim</em></a> monthly indicator for EU28 area</strong>:</p>
<p><img src="docs/tour_occ_nim_table.png"></p>
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<p>We suppose here that all required packages have been already install (see <code>Prophet</code> original webpage for <code>Prophet</code>'s dependencies). Let us first import everything we need:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">requests</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">fbprophet</span> <span class="k">import</span> <span class="n">Prophet</span>
<span class="c1"># needed for display in notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> notebook
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
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<p>We then need to retrieve the historical data of <em>tour_occ_nim</em> EU28 time-series that will be "fed" to the model. We will use <a href="http://ec.europa.eu/eurostat/web/json-and-unicode-web-services/getting-started/rest-request">Eurostat REST API</a> for that purpose.</p>
<p>We first "configure" the API. Note that we will manipulate <code>json</code> data:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">PROTOCOL</span> <span class="o">=</span> <span class="s2">"http"</span>
<span class="n">API_LANG</span> <span class="o">=</span> <span class="s2">"en"</span>
<span class="n">API_FMT</span> <span class="o">=</span> <span class="s2">"json"</span>
<span class="n">API_DOMAIN</span> <span class="o">=</span> <span class="s1">'ec.europa.eu/eurostat/wdds'</span>
<span class="n">API_VERS</span> <span class="o">=</span> <span class="mf">2.1</span>
<span class="n">API_URL</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">://</span><span class="si">{}</span><span class="s2">/rest/data/v</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">PROTOCOL</span><span class="p">,</span> <span class="n">API_DOMAIN</span><span class="p">,</span> <span class="n">API_VERS</span><span class="p">,</span> <span class="n">API_FMT</span><span class="p">,</span> <span class="n">API_LANG</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">API_URL</span><span class="p">)</span>
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<pre>http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en
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<p>We then define the specific time-series that we will consider through the following settings:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">GEO</span> <span class="o">=</span> <span class="s2">"EU28"</span>
<span class="c1"># TIME : all</span>
<span class="n">INDICATOR</span> <span class="o">=</span> <span class="p">(</span><span class="s1">u'tour_occ_nim'</span><span class="p">,</span> <span class="s2">"Tour accomodation"</span><span class="p">)</span>
<span class="n">UNIT</span> <span class="o">=</span> <span class="p">(</span><span class="s1">u'NR'</span><span class="p">,</span> <span class="s2">"Number of nights"</span><span class="p">)</span>
<span class="n">NACE_R2</span> <span class="o">=</span> <span class="p">(</span><span class="s1">u'I551'</span><span class="p">,</span> <span class="s2">"Hotels; holiday and other short-stay accommodation..."</span><span class="p">)</span>
<span class="n">INDIC_TO</span> <span class="o">=</span> <span class="p">(</span><span class="s1">u'B006'</span><span class="p">,</span> <span class="s2">"Nights spent, total"</span><span class="p">)</span>
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<p>We finally build the URL that we will submit to the REST API (see also <a href="http://ec.europa.eu/eurostat/web/json-and-unicode-web-services/getting-started/query-builder">Eurostat query builder</a>):</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">url</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">?geo=</span><span class="si">{}</span><span class="s2">&unit=</span><span class="si">{}</span><span class="s2">&nace_r2=</span><span class="si">{}</span><span class="s2">&indic_to=</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">API_URL</span><span class="p">,</span> <span class="n">INDICATOR</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">GEO</span><span class="p">,</span> <span class="n">UNIT</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">NACE_R2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">INDIC_TO</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
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<pre>http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/tour_occ_nim?geo=EU28&unit=NR&nace_r2=I551&indic_to=B006
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<p>If you copy paste this URL in your browser as is, you can display the data as they are returned by the REST service, in <a href="https://json-stat.org"><code>jsonstat</code></a> format:</p>
<p><img src="docs/tour_occ_nim_json.png"></p>
<p>Next, we want to store the data in a <code>pandas.DataFrame</code> object (since the input to <code>Prophet</code> is always a <code>pandas.DataFrame</code>). From here, it should be possible to perform this operation using any of <a href="https://pypi.python.org/pypi/pyjstat/"><code>pyjstat</code></a> or <a href="https://github.com/26fe/jsonstat.py"><code>jsonstat.py</code></a> dedicated packages, <em>e.g.</em>:</p>
<div class="highlight"><pre><span></span><span class="n">collection</span> <span class="o">=</span> <span class="n">jsonstat</span><span class="o">.</span><span class="n">from_url</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
<span class="n">tour_occ_nim</span> <span class="o">=</span> <span class="n">collection</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span><span class="s1">'tour_occ_nim'</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">tour_occ_nim</span><span class="o">.</span><span class="n">to_data_frame</span><span class="p">(</span><span class="s1">'year'</span><span class="p">)</span>
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<p>However, it seems that neither package currently works with <code>Python3</code>.</p>
<p>Let us then do the job "manually". We submit the request directly to the REST service with the built URL:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">session</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">session</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">response</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
<span class="n">response</span><span class="o">.</span><span class="n">raise_for_status</span><span class="p">()</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s2">"ERROR: wrong request formulated"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">"OK: status=</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">status_code</span><span class="p">))</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">response</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s1">'error retrieveing response from URL'</span><span class="p">)</span>
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<pre>INFO:requests.packages.urllib3.connectionpool:Starting new HTTP connection (1): ec.europa.eu
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<pre>OK: status=200
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<p>Once we have retrieved the response, we further extract/format the data of interest from the formatted <code>jsonstat</code> dictionary:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">resp</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
<span class="n">lbl2idx</span> <span class="o">=</span> <span class="n">resp</span><span class="p">[</span><span class="s1">'dimension'</span><span class="p">][</span><span class="s1">'time'</span><span class="p">][</span><span class="s1">'category'</span><span class="p">][</span><span class="s1">'index'</span><span class="p">]</span>
<span class="n">idx2lbl</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span><span class="n">k</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">lbl2idx</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">resp</span><span class="p">[</span><span class="s1">'value'</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="n">idx2lbl</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">)]:</span><span class="n">v</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="n">table</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'M'</span><span class="p">,</span><span class="s1">'-'</span><span class="p">):</span><span class="n">v</span> <span class="k">for</span> <span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">v</span><span class="p">)</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
</pre></div>
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<p>The table contains exactly the data we need, let us store it into a <code>pandas.DataFrame</code> object as desired. As already mentioned, the input to <code>Prophet</code> is always a <code>pandas.DataFrame</code> object, and it must contain two columns: <code>ds</code> and <code>y</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">items</span><span class="p">()),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'ds'</span><span class="p">,</span><span class="s1">'y'</span><span class="p">])</span>
<span class="c1"># df = df[df['y'].notnull()] # Prophet can deal with NaN data!</span>
</pre></div>
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<p>We also sort the data "chronogically" (this is not really neede since the sorting is also done in <code>Prophet</code>, but it will be useful for plotting):</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'ds'</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ds_last</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'ds'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># we keep that for later</span>
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<p>Let us have a quick look at the data:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>
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<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ds</th>
<th>y</th>
</tr>
</thead>
<tbody>
<tr>
<th>18</th>
<td>2005-01</td>
<td>80813747</td>
</tr>
<tr>
<th>141</th>
<td>2005-02</td>
<td>87420210</td>
</tr>
<tr>
<th>5</th>
<td>2005-03</td>
<td>108217408</td>
</tr>
<tr>
<th>137</th>
<td>2005-04</td>
<td>103711903</td>
</tr>
<tr>
<th>128</th>
<td>2005-05</td>
<td>134257150</td>
</tr>
</tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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<div class="output_area"><div class="prompt output_prompt">Out[10]:</div>
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<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ds</th>
<th>y</th>
</tr>
</thead>
<tbody>
<tr>
<th>138</th>
<td>2016-07</td>
<td>237181783</td>
</tr>
<tr>
<th>109</th>
<td>2016-08</td>
<td>252210220</td>
</tr>
<tr>
<th>56</th>
<td>2016-09</td>
<td>193509848</td>
</tr>
<tr>
<th>7</th>
<td>2016-10</td>
<td>155886177</td>
</tr>
<tr>
<th>107</th>
<td>2016-11</td>
<td>103648347</td>
</tr>
</tbody>
</table>
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<p>Last, we further convert the <code>ds</code> column into <code>datetime</code> formatted values:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">'ds'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'ds'</span><span class="p">])</span> <span class="c1"># note that this will add a day date</span>
<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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<div class="output_area"><div class="prompt output_prompt">Out[11]:</div>
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<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ds</th>
<th>y</th>
</tr>
</thead>
<tbody>
<tr>
<th>138</th>
<td>2016-07-01</td>
<td>237181783</td>
</tr>
<tr>
<th>109</th>
<td>2016-08-01</td>
<td>252210220</td>
</tr>
<tr>
<th>56</th>
<td>2016-09-01</td>
<td>193509848</td>
</tr>
<tr>
<th>7</th>
<td>2016-10-01</td>
<td>155886177</td>
</tr>
<tr>
<th>107</th>
<td>2016-11-01</td>
<td>103648347</td>
</tr>
</tbody>
</table>
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<p>Let us display the data (and check the seasonal nature of it):</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xlabel</span> <span class="o">=</span> <span class="s2">"Time"</span>
<span class="n">ylabel</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{}</span><span class="s2"> : </span><span class="si">{}</span><span class="s2"> - </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">INDICATOR</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">INDIC_TO</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">GEO</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'ds'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="s1">'k.'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'ds'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'#0072B2'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">'major'</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'gray'</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Historical data (last: </span><span class="si">{}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ds_last</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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// Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,
// which will in turn request a refresh of the image.
this.send_message('resize', {'width': x_pixels, 'height': y_pixels});
}
mpl.figure.prototype.send_message = function(type, properties) {
properties['type'] = type;
properties['figure_id'] = this.id;
this.ws.send(JSON.stringify(properties));
}
mpl.figure.prototype.send_draw_message = function() {
if (!this.waiting) {
this.waiting = true;
this.ws.send(JSON.stringify({type: "draw", figure_id: this.id}));
}
}
mpl.figure.prototype.handle_save = function(fig, msg) {
var format_dropdown = fig.format_dropdown;
var format = format_dropdown.options[format_dropdown.selectedIndex].value;
fig.ondownload(fig, format);
}
mpl.figure.prototype.handle_resize = function(fig, msg) {
var size = msg['size'];
if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {
fig._resize_canvas(size[0], size[1]);
fig.send_message("refresh", {});
};
}
mpl.figure.prototype.handle_rubberband = function(fig, msg) {
var x0 = msg['x0'];
var y0 = fig.canvas.height - msg['y0'];
var x1 = msg['x1'];
var y1 = fig.canvas.height - msg['y1'];
x0 = Math.floor(x0) + 0.5;
y0 = Math.floor(y0) + 0.5;
x1 = Math.floor(x1) + 0.5;
y1 = Math.floor(y1) + 0.5;
var min_x = Math.min(x0, x1);
var min_y = Math.min(y0, y1);
var width = Math.abs(x1 - x0);
var height = Math.abs(y1 - y0);
fig.rubberband_context.clearRect(
0, 0, fig.canvas.width, fig.canvas.height);
fig.rubberband_context.strokeRect(min_x, min_y, width, height);
}
mpl.figure.prototype.handle_figure_label = function(fig, msg) {
// Updates the figure title.
fig.header.textContent = msg['label'];
}
mpl.figure.prototype.handle_cursor = function(fig, msg) {
var cursor = msg['cursor'];
switch(cursor)
{
case 0:
cursor = 'pointer';
break;
case 1:
cursor = 'default';
break;
case 2:
cursor = 'crosshair';
break;
case 3:
cursor = 'move';
break;
}
fig.rubberband_canvas.style.cursor = cursor;
}
mpl.figure.prototype.handle_message = function(fig, msg) {
fig.message.textContent = msg['message'];
}
mpl.figure.prototype.handle_draw = function(fig, msg) {
// Request the server to send over a new figure.
fig.send_draw_message();
}
mpl.figure.prototype.handle_image_mode = function(fig, msg) {
fig.image_mode = msg['mode'];
}
mpl.figure.prototype.updated_canvas_event = function() {
// Called whenever the canvas gets updated.
this.send_message("ack", {});
}
// A function to construct a web socket function for onmessage handling.
// Called in the figure constructor.
mpl.figure.prototype._make_on_message_function = function(fig) {
return function socket_on_message(evt) {
if (evt.data instanceof Blob) {
/* FIXME: We get "Resource interpreted as Image but
* transferred with MIME type text/plain:" errors on
* Chrome. But how to set the MIME type? It doesn't seem
* to be part of the websocket stream */
evt.data.type = "image/png";
/* Free the memory for the previous frames */
if (fig.imageObj.src) {
(window.URL || window.webkitURL).revokeObjectURL(
fig.imageObj.src);
}
fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(
evt.data);
fig.updated_canvas_event();
fig.waiting = false;
return;
}
else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == "data:image/png;base64") {
fig.imageObj.src = evt.data;
fig.updated_canvas_event();
fig.waiting = false;
return;
}
var msg = JSON.parse(evt.data);
var msg_type = msg['type'];
// Call the "handle_{type}" callback, which takes
// the figure and JSON message as its only arguments.
try {
var callback = fig["handle_" + msg_type];
} catch (e) {
console.log("No handler for the '" + msg_type + "' message type: ", msg);
return;
}
if (callback) {
try {
// console.log("Handling '" + msg_type + "' message: ", msg);
callback(fig, msg);
} catch (e) {
console.log("Exception inside the 'handler_" + msg_type + "' callback:", e, e.stack, msg);
}