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<li class="toctree-l4"><a class="reference internal" href="#mindradar.IFFT"><code class="docutils literal notranslate"><span class="pre">mindradar.IFFT</span></code></a></li>
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<section id="ifft">
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<h1>IFFT<a class="headerlink" href="#ifft" title="此标题的永久链接"></a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mindradar.IFFT">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mindradar.</span></span><span class="sig-name descname"><span class="pre">IFFT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'backward'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mindradar.IFFT" title="永久链接至目标"></a></dt>
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<dd><p>傅里叶变换IFFT。计算表达式为:</p>
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<div class="math notranslate nohighlight">
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\[X[\omega_1, \dots, \omega_d] =
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\frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
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e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},\]</div>
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<p>其中， <span class="math notranslate nohighlight">\(d\)</span> = <cite>signal_ndim</cite> 是信号的维度，<span class="math notranslate nohighlight">\(N_i\)</span> 则是信号第 <span class="math notranslate nohighlight">\(i\)</span> 个维度的大小。</p>
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<dl class="simple">
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<dt>参数：</dt><dd><ul class="simple">
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<li><p><strong>n</strong> (int，可选) - 输出转换轴的长度。如果n小于输入的长度，则裁剪输入。如果它更大，则输入将填充零。如果未给出n，则使用沿轴指定的轴的输入长度。默认值： <code class="docutils literal notranslate"><span class="pre">-1</span></code> 。</p></li>
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<li><p><strong>dim</strong> (int，可选) - 计算FFT的轴。如果没有给出，则使用最后一个轴。默认值： <code class="docutils literal notranslate"><span class="pre">-1</span></code> 。</p></li>
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<li><p><strong>norm</strong> (str，可选) - 表示该操作的规范化方式，可选值：[ <code class="docutils literal notranslate"><span class="pre">"backward"</span></code> , <code class="docutils literal notranslate"><span class="pre">"forward"</span></code> , <code class="docutils literal notranslate"><span class="pre">"ortho"</span></code> ]。默认值： <code class="docutils literal notranslate"><span class="pre">"backward"</span></code> 。</p>
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<ul>
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<li><p>“backward”，正向变换不缩放，逆变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，其中 <cite>n</cite> 表示输入 <cite>x</cite> 的元素数量。</p></li>
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<li><p>“ortho”，正向变换与逆变换均按 <span class="math notranslate nohighlight">\(1/\sqrt n\)</span> 缩放。</p></li>
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<li><p>“forward”，正向变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，逆变换不缩放。</p></li>
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</ul>
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</li>
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</ul>
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</dd>
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<dt>输入：</dt><dd><ul class="simple">
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<li><p><strong>x</strong> (Tensor) - 输入Tensor的维数必须大于或等于 <cite>dim</cite> 。数据类型应为复数。</p></li>
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</ul>
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</dd>
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<dt>输出：</dt><dd><p>Tensor，表示复数到复数傅里叶变换IFFT的结果。</p>
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</dd>
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<dt>支持平台：</dt><dd><p><code class="docutils literal notranslate"><span class="pre">CPU</span></code></p>
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</dd>
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</dl>
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<div class="admonition note">
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<p class="admonition-title">备注</p>
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<p>IFFT要求complex64或complex128类型的输入，返回complex64或complex128类型的输出。</p>
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</div>
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<p>示例：</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos">1</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
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<span class="linenos">2</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindradar</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mr</span>
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<span class="linenos">3</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
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<span class="linenos">4</span>
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<span class="linenos">5</span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">3</span><span class="n">j</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
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<span class="linenos">6</span><span class="n">x_tensor</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="hll"><span class="linenos">7</span><span class="n">ifft</span> <span class="o">=</span> <span class="n">mr</span><span class="o">.</span><span class="n">IFFT</span><span class="p">()</span>
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</span><span class="linenos">8</span><span class="n">out</span> <span class="o">=</span> <span class="n">ifft</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">)</span>
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<span class="linenos">9</span><span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
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