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Profiling
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‎README.md

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0.34986
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```
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### Timing by Function
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#### Generates a PNG image of a call graph with highlighted bottlenecks:
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```python
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# $ pip3 install pycallgraph
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from pycallgraph import output, PyCallGraph
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from datetime import datetime
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time_str = datetime.now().strftime('%Y%m%d%H%M%S')
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filename = f'profile-{time_str}.png'
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drawer = output.GraphvizOutput(output_file=filename)
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with PyCallGraph(drawer):
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<code_to_be_profiled>
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```
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### Profiling by Line
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```text
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$ pip3 install line_profiler memory_profiler
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```python
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# $ pip3 install line_profiler memory_profiler
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@profile
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def main():
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a = [*range(10000)]
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b = {*range(10000)}
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main()
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```
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```text
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4 36.160 MiB 0.426 MiB b = {*range(10000)}
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```
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### Call Graph
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#### Generates a PNG image of a call graph with highlighted bottlenecks:
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```python
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# $ pip3 install pycallgraph
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from pycallgraph import output, PyCallGraph
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from datetime import datetime
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time_str = datetime.now().strftime('%Y%m%d%H%M%S')
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filename = f'profile-{time_str}.png'
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drawer = output.GraphvizOutput(output_file=filename)
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with PyCallGraph(drawer):
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<code_to_be_profiled>
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```
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NumPy
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‎index.html

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<span class="hljs-number">0.34986</span>
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</code></pre></div>
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<div><h3 id="timingbyfunction">Timing by Function</h3><div><h4 id="generatesapngimageofacallgraphwithhighlightedbottlenecks">Generates a PNG image of a call graph with highlighted bottlenecks:</h4><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pycallgraph</span>
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<span class="hljs-keyword">from</span> pycallgraph <span class="hljs-keyword">import</span> output, PyCallGraph
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<span class="hljs-keyword">from</span> datetime <span class="hljs-keyword">import</span> datetime
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time_str = datetime.now().strftime(<span class="hljs-string">'%Y%m%d%H%M%S'</span>)
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filename = <span class="hljs-string">f'profile-<span class="hljs-subst">{time_str}</span>.png'</span>
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drawer = output.GraphvizOutput(output_file=filename)
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<span class="hljs-keyword">with</span> PyCallGraph(drawer):
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&lt;code_to_be_profiled&gt;
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</code></pre></div></div>
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<div><h3 id="profilingbyline">Profiling by Line</h3><pre><code class="text language-text">$ pip3 install line_profiler memory_profiler
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<div><h3 id="profilingbyline">Profiling by Line</h3><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install line_profiler memory_profiler</span>
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<span class="hljs-meta">@profile</span>
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<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">main</span><span class="hljs-params">()</span>:</span>
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a = [*range(<span class="hljs-number">10000</span>)]
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b = {*range(<span class="hljs-number">10000</span>)}
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main()
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</code></pre></div>
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<pre><code class="text language-text">$ kernprof -lv test.py
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3 35.734 MiB 0.348 MiB a = [*range(10000)]
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4 36.160 MiB 0.426 MiB b = {*range(10000)}
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</code></pre>
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<div><h3 id="callgraph">Call Graph</h3><div><h4 id="generatesapngimageofacallgraphwithhighlightedbottlenecks">Generates a PNG image of a call graph with highlighted bottlenecks:</h4><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pycallgraph</span>
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<span class="hljs-keyword">from</span> pycallgraph <span class="hljs-keyword">import</span> output, PyCallGraph
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<span class="hljs-keyword">from</span> datetime <span class="hljs-keyword">import</span> datetime
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time_str = datetime.now().strftime(<span class="hljs-string">'%Y%m%d%H%M%S'</span>)
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filename = <span class="hljs-string">f'profile-<span class="hljs-subst">{time_str}</span>.png'</span>
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drawer = output.GraphvizOutput(output_file=filename)
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<span class="hljs-keyword">with</span> PyCallGraph(drawer):
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&lt;code_to_be_profiled&gt;
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</code></pre></div></div>
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<div><h2 id="numpy"><a href="#numpy" name="numpy">#</a>NumPy</h2><p><strong>Array manipulation mini language. Can run up to one hundred times faster than equivalent Python code.</strong></p><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install numpy</span>
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<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
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</code></pre></div>

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