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---
title: Test
keywords: fastai
sidebar: home_sidebar
summary: "Helper functions to quickly write tests in notebooks"
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.showdoc</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
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<h2 id="Simple-test-functions">Simple test functions<a class="anchor-link" href="#Simple-test-functions">¶</a></h2>
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<p>We can check that code raises an exception when that's expected (<a href="/test.html#test_fail"><code>test_fail</code></a>). To test for equality or inequality (with different types of things) we define a simple funciton <a href="/test.html"><code>test</code></a> that compares two object with a given <code>cmp</code> operator.</p>
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<h4 id="test_fail" class="doc_header"><code>test_fail</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L10" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_fail</code>(<strong><code>f</code></strong>, <strong><code>msg</code></strong>=<em><code>''</code></em>, <strong><code>contains</code></strong>=<em><code>''</code></em>)</p>
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<p>Fails with <code>msg</code> unless <code>f()</code> raises an exception and (optionally) has <code>contains</code> in <code>e.args</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">_fail</span><span class="p">():</span> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"foobar"</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="n">_fail</span><span class="p">,</span> <span class="n">contains</span><span class="o">=</span><span class="s2">"foo"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_fail</span><span class="p">():</span> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">()</span>
<span class="n">test_fail</span><span class="p">(</span><span class="n">_fail</span><span class="p">)</span>
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<h4 id="test" class="doc_header"><code>test</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L19" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>, <strong><code>cmp</code></strong>, <strong><code>cname</code></strong>=<em><code>None</code></em>)</p>
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<p><code>assert</code> that <code>cmp(a,b)</code>; display inputs and <code>cname or cmp.__name__</code> if it fails</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">operator</span><span class="o">.</span><span class="n">eq</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">],</span> <span class="n">operator</span><span class="o">.</span><span class="n">eq</span><span class="p">))</span>
<span class="n">test</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">],</span> <span class="n">operator</span><span class="o">.</span><span class="n">ne</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">operator</span><span class="o">.</span><span class="n">ne</span><span class="p">))</span>
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<h4 id="all_equal" class="doc_header"><code>all_equal</code><a href="https://github.com/fastai/fastai/tree/master/fastai/core/imports.py#L52" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>all_equal</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p>Compares whether <code>a</code> and <code>b</code> are the same length and have the same contents</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span><span class="p">([</span><span class="s1">'abc'</span><span class="p">],</span> <span class="p">[</span><span class="s1">'abc'</span><span class="p">],</span> <span class="n">all_equal</span><span class="p">)</span>
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<h4 id="equals" class="doc_header"><code>equals</code><a href="https://github.com/fastai/fastai/tree/master/fastai/core/imports.py#L67" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>equals</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p>Compares <code>a</code> and <code>b</code> for equality; supports sublists, tensors and arrays too</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span><span class="p">([[</span><span class="s1">'abc'</span><span class="p">],[</span><span class="s1">'a'</span><span class="p">]],</span> <span class="p">[[</span><span class="s1">'abc'</span><span class="p">],[</span><span class="s1">'a'</span><span class="p">]],</span> <span class="n">equals</span><span class="p">)</span>
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<h4 id="nequals" class="doc_header"><code>nequals</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L25" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>nequals</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p>Compares <code>a</code> and <code>b</code> for <code>not equals</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span><span class="p">([</span><span class="s1">'abc'</span><span class="p">],</span> <span class="p">[</span><span class="s1">'ab'</span> <span class="p">],</span> <span class="n">nequals</span><span class="p">)</span>
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<h2 id="test_eq-test_ne,-etc...">test_eq test_ne, etc...<a class="anchor-link" href="#test_eq-test_ne,-etc...">¶</a></h2>
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<p>Just use <a href="/test.html#test_eq"><code>test_eq</code></a>/<a href="/test.html#test_ne"><code>test_ne</code></a> to test for <code>==</code>/<code>!=</code>. <a href="/test.html#test_eq_type"><code>test_eq_type</code></a> check things are equals and of the same type. We define them using <a href="/test.html"><code>test</code></a>:</p>
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<h4 id="test_eq" class="doc_header"><code>test_eq</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L30" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_eq</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p><a href="/test.html"><code>test</code></a> that <code>a==b</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span><span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">([</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="mi">3</span><span class="p">],[</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="mi">3</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">b</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="nb">dict</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_eq</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span><span class="p">),</span> <span class="n">contains</span><span class="o">=</span><span class="s2">"=="</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">({</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">},</span> <span class="p">{</span><span class="s1">'c'</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df1</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">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span><span class="n">b</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">]))</span>
<span class="n">df2</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">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span><span class="n">b</span><span class="o">=</span><span class="p">[</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span><span class="n">df2</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">df1</span><span class="o">.</span><span class="n">a</span><span class="p">,</span><span class="n">df2</span><span class="o">.</span><span class="n">a</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">T</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span> <span class="k">pass</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">T</span><span class="p">(</span><span class="n">df2</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<h4 id="test_eq_type" class="doc_header"><code>test_eq_type</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L35" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_eq_type</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p><a href="/test.html"><code>test</code></a> that <code>a==b</code> and are same type</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq_type</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_eq_type</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.</span><span class="p">))</span>
<span class="n">test_eq_type</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_eq_type</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)))</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_eq_type</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.</span><span class="p">]))</span>
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<h4 id="test_ne" class="doc_header"><code>test_ne</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L42" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_ne</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p><a href="/test.html"><code>test</code></a> that <code>a!=b</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_ne</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">test_ne</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
<span class="n">test_ne</span><span class="p">(</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]))</span>
<span class="n">test_ne</span><span class="p">(</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]))</span>
<span class="n">test_ne</span><span class="p">([</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]),</span><span class="mi">3</span><span class="p">],[</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])])</span>
<span class="n">test_ne</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">]))</span>
<span class="n">test_ne</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">]))</span>
<span class="n">test_ne</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">b</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">])</span>
<span class="n">test_ne</span><span class="p">([</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">],</span> <span class="nb">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">b</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
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<h4 id="is_close" class="doc_header"><code>is_close</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L47" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>is_close</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>, <strong><code>eps</code></strong>=<em><code>1e-05</code></em>)</p>
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<p>Is <code>a</code> within <code>eps</code> of <code>b</code></p>
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<h4 id="test_close" class="doc_header"><code>test_close</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L56" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_close</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>, <strong><code>eps</code></strong>=<em><code>1e-05</code></em>)</p>
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<p><a href="/test.html"><code>test</code></a> that <code>a</code> is within <code>eps</code> of <code>b</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_close</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.001</span><span class="p">,</span><span class="n">eps</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_close</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.001</span><span class="p">))</span>
<span class="n">test_close</span><span class="p">([</span><span class="o">-</span><span class="mf">0.001</span><span class="p">,</span><span class="mf">1.001</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span><span class="mf">1.</span><span class="p">],</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
<span class="n">test_close</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.001</span><span class="p">,</span><span class="mf">1.001</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span><span class="mf">1.</span><span class="p">]),</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
<span class="n">test_close</span><span class="p">(</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">0.001</span><span class="p">,</span><span class="mf">1.001</span><span class="p">]),</span> <span class="n">array</span><span class="p">([</span><span class="mf">0.</span><span class="p">,</span><span class="mf">1.</span><span class="p">]),</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
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<h4 id="test_is" class="doc_header"><code>test_is</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L61" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_is</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p><a href="/test.html"><code>test</code></a> that <code>a is b</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_is</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
<span class="n">a</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_is</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
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<h4 id="test_shuffled" class="doc_header"><code>test_shuffled</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L66" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_shuffled</code>(<strong><code>a</code></strong>, <strong><code>b</code></strong>)</p>
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<p><a href="/test.html"><code>test</code></a> that <code>a</code> and <code>b</code> are shuffled versions of the same sequence of items</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">))</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="n">test_shuffled</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span><span class="n">test_shuffled</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">a</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="s1">'abc'</span>
<span class="n">b</span> <span class="o">=</span> <span class="s1">'abcabc'</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span><span class="n">test_shuffled</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="mi">42</span><span class="p">,</span> <span class="kc">True</span><span class="p">]</span>
<span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="mi">42</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'a'</span><span class="p">]</span>
<span class="n">test_shuffled</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
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<h4 id="test_stdout" class="doc_header"><code>test_stdout</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L72" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_stdout</code>(<strong><code>f</code></strong>, <strong><code>exp</code></strong>, <strong><code>regex</code></strong>=<em><code>False</code></em>)</p>
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<p>Test that <code>f</code> prints <code>exp</code> to stdout, optionally checking as <code>regex</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_stdout</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">'hi'</span><span class="p">),</span> <span class="s1">'hi'</span><span class="p">)</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_stdout</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">'hi'</span><span class="p">),</span> <span class="s1">'ho'</span><span class="p">))</span>
<span class="n">test_stdout</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="mi">1</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span>
<span class="n">test_stdout</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">'hi there!'</span><span class="p">),</span> <span class="sa">r</span><span class="s1">'^hi.*!$'</span><span class="p">,</span> <span class="n">regex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<h4 id="test_warns" class="doc_header"><code>test_warns</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L80" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_warns</code>(<strong><code>f</code></strong>, <strong><code>show</code></strong>=<em><code>False</code></em>)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_warns</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Oh no!"</span><span class="p">),</span> <span class="p">{})</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">test_warns</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="mi">2</span><span class="o">+</span><span class="mi">2</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_warns</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Oh no!"</span><span class="p">),</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre><class 'UserWarning'>: Oh no!
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">TEST_IMAGE</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">128</span><span class="p">,</span><span class="mi">128</span><span class="p">));</span> <span class="n">im</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">TEST_IMAGE_BW</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">128</span><span class="p">,</span><span class="mi">128</span><span class="p">));</span> <span class="n">im</span>
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<img src="data:image/png;base64,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<h4 id="test_fig_exists" class="doc_header"><code>test_fig_exists</code><a href="https://github.com/fastai/fastai/tree/master/fastai/test.py#L94" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>test_fig_exists</code>(<strong><code>ax</code></strong>)</p>
</blockquote>
<p>Test there is a figure displayed in <code>ax</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">array</span><span class="p">(</span><span class="n">im</span><span class="p">));</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_fig_exists</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
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