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<h1 id="scientific-python-cheatsheet">Scientific Python Cheatsheet</h1>
<!-- markdown-toc start - Don't edit this section. Run M-x markdown-toc-generate-toc again -->
<p><strong>Table of Contents</strong></p>
<ul>
<li><a href="#scientific-python-cheatsheet">Scientific Python Cheatsheet</a>
<ul>
<li><a href="#pure-python">Pure Python</a>
<ul>
<li><a href="#types">Types</a></li>
<li><a href="#lists">Lists</a></li>
<li><a href="#dictionaries">Dictionaries</a></li>
<li><a href="#strings">Strings</a></li>
<li><a href="#operators">Operators</a></li>
<li><a href="#control-flow">Control Flow</a></li>
<li><a href="#functions-classes-generators-decorators">Functions, Classes, Generators, Decorators</a></li>
</ul></li>
<li><a href="#numpy">NumPy</a>
<ul>
<li><a href="#array-initialization">array initialization</a></li>
<li><a href="#reading-writing-files">reading/ writing files</a></li>
<li><a href="#array-properties-and-operations">array properties and operations</a></li>
<li><a href="#indexing">indexing</a></li>
<li><a href="#boolean-arrays">boolean arrays</a></li>
<li><a href="#elementwise-operations-and-math-functions">elementwise operations and math functions</a></li>
<li><a href="#inner--outer-products">inner / outer products</a></li>
<li><a href="#interpolation-integration">interpolation, integration</a></li>
<li><a href="#fft">fft</a></li>
<li><a href="#rounding">rounding</a></li>
<li><a href="#random-variables">random variables</a></li>
</ul></li>
<li><a href="#matplotlib">Matplotlib</a>
<ul>
<li><a href="#figures-and-axes">figures and axes</a></li>
<li><a href="#figures-and-axes-properties">figures and axes properties</a></li>
<li><a href="#plotting-routines">plotting routines</a></li>
</ul></li>
</ul></li>
</ul>
<!-- markdown-toc end -->
<h2 id="pure-python">Pure Python</h2>
<h3 id="types">Types</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">=</span> <span class="dv">2</span> <span class="co"># integer</span>
b <span class="op">=</span> <span class="fl">5.0</span> <span class="co"># float</span>
c <span class="op">=</span> <span class="fl">8.3e5</span> <span class="co"># exponential</span>
d <span class="op">=</span> <span class="fl">1.5</span> <span class="op">+</span><span class="ot"> 0.5j</span> <span class="co"># complex</span>
e <span class="op">=</span> <span class="dv">4</span> <span class="op">></span> <span class="dv">5</span> <span class="co"># boolean</span>
f <span class="op">=</span> <span class="st">'word'</span> <span class="co"># string</span></code></pre></div>
<h3 id="lists">Lists</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">=</span> [<span class="st">'red'</span>, <span class="st">'blue'</span>, <span class="st">'green'</span>] <span class="co"># manually initialization</span>
b <span class="op">=</span> <span class="bu">range</span>(<span class="dv">5</span>) <span class="co"># initialization through a function</span>
c <span class="op">=</span> [nu<span class="op">**</span><span class="dv">2</span> <span class="cf">for</span> nu <span class="op">in</span> b] <span class="co"># initialize through list comprehension</span>
d <span class="op">=</span> [nu<span class="op">**</span><span class="dv">2</span> <span class="cf">for</span> nu <span class="op">in</span> b <span class="cf">if</span> b <span class="op"><</span> <span class="dv">3</span>] <span class="co"># list comprehension withcondition</span>
e <span class="op">=</span> c[<span class="dv">0</span>] <span class="co"># access element</span>
f <span class="op">=</span> e[<span class="dv">1</span>: <span class="dv">2</span>] <span class="co"># access a slice of the list</span>
g <span class="op">=</span> [<span class="st">'re'</span>, <span class="st">'bl'</span>] <span class="op">+</span> [<span class="st">'gr'</span>] <span class="co"># list concatenation</span>
h <span class="op">=</span> [<span class="st">'re'</span>] <span class="op">*</span> <span class="dv">5</span> <span class="co"># repeat a list</span>
[<span class="st">'re'</span>, <span class="st">'bl'</span>].index(<span class="st">'re'</span>) <span class="co"># returns index of 're'</span>
<span class="co">'re'</span> <span class="op">in</span> [<span class="st">'re'</span>, <span class="st">'bl'</span>] <span class="co"># true if 're' in list</span>
<span class="bu">sorted</span>([<span class="dv">3</span>, <span class="dv">2</span>, <span class="dv">1</span>]) <span class="co"># returns sorted list</span>
z <span class="op">=</span> [<span class="st">'red'</span>] <span class="op">+</span> [<span class="st">'green'</span>, <span class="st">'blue'</span>] <span class="co"># list concatenation</span></code></pre></div>
<h3 id="dictionaries">Dictionaries</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">=</span> {<span class="st">'red'</span>: <span class="st">'rouge'</span>, <span class="st">'blue'</span>: <span class="st">'bleu'</span>, <span class="st">'green'</span>: <span class="st">'vert'</span>} <span class="co"># dictionary</span>
b <span class="op">=</span> a[<span class="st">'red'</span>] <span class="co"># translate item</span>
c <span class="op">=</span> [value <span class="cf">for</span> key, value <span class="op">in</span> b.items()] <span class="co"># loop through contents</span>
d <span class="op">=</span> a.get(<span class="st">'yellow'</span>, <span class="st">'no translation found'</span>) <span class="co"># return default</span></code></pre></div>
<h3 id="strings">Strings</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">=</span> <span class="st">'red'</span> <span class="co"># assignment</span>
char <span class="op">=</span> a[<span class="dv">2</span>] <span class="co"># access individual characters</span>
<span class="co">'red '</span> <span class="op">+</span> <span class="st">'blue'</span> <span class="co"># string concatenation</span>
<span class="co">'1, 2, three'</span>.split(<span class="st">','</span>) <span class="co"># split string into list</span>
<span class="co">'.'</span>.join([<span class="st">'1'</span>, <span class="st">'2'</span>, <span class="st">'three'</span>]) <span class="co"># concatenate list into string</span></code></pre></div>
<h3 id="operators">Operators</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">=</span> <span class="dv">2</span> <span class="co"># assignment</span>
a <span class="op">+=</span> <span class="dv">1</span> (<span class="op">*=</span>, <span class="op">/=</span>) <span class="co"># change and assign</span>
<span class="dv">3</span> <span class="op">+</span> <span class="dv">2</span> <span class="co"># addition</span>
<span class="dv">3</span> <span class="op">/</span> <span class="dv">2</span> <span class="co"># integer division (python2) or float division (python3)</span>
<span class="dv">3</span> <span class="op">//</span> <span class="dv">2</span> <span class="co"># integer division</span>
<span class="dv">3</span> <span class="op">*</span> <span class="dv">2</span> <span class="co"># multiplication</span>
<span class="dv">3</span> <span class="op">**</span> <span class="dv">2</span> <span class="co"># exponent</span>
<span class="dv">3</span> <span class="op">%</span> <span class="dv">2</span> <span class="co"># remainder</span>
<span class="bu">abs</span>() <span class="co"># absolute value</span>
<span class="dv">1</span> <span class="op">==</span> <span class="dv">1</span> <span class="co"># equal</span>
<span class="dv">2</span> <span class="op">></span> <span class="dv">1</span> <span class="co"># larger</span>
<span class="dv">2</span> <span class="op"><</span> <span class="dv">1</span> <span class="co"># smaller</span>
<span class="dv">1</span> <span class="op">!=</span> <span class="dv">2</span> <span class="co"># not equal</span>
<span class="dv">1</span> <span class="op">!=</span> <span class="dv">2</span> <span class="op">and</span> <span class="dv">2</span> <span class="op"><</span> <span class="dv">3</span> <span class="co"># logical AND</span>
<span class="dv">1</span> <span class="op">!=</span> <span class="dv">2</span> <span class="op">or</span> <span class="dv">2</span> <span class="op"><</span> <span class="dv">3</span> <span class="co"># logical OR</span>
<span class="op">not</span> <span class="dv">1</span> <span class="op">==</span> <span class="dv">2</span> <span class="co"># logical NOT</span>
a <span class="op">in</span> b <span class="co"># test if a is in b</span>
a <span class="op">is</span> b <span class="co"># test if objects point to the same memory (id)</span></code></pre></div>
<h3 id="control-flow">Control Flow</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="co"># if/elif/else</span>
a, b <span class="op">=</span> <span class="dv">1</span>, <span class="dv">2</span>
<span class="cf">if</span> a <span class="op">+</span> b <span class="op">==</span> <span class="dv">3</span>:
<span class="bu">print</span> <span class="st">'True'</span>
<span class="cf">elif</span> a <span class="op">+</span> b <span class="op">==</span> <span class="dv">1</span>:
<span class="bu">print</span> <span class="st">'False'</span>
<span class="cf">else</span>:
<span class="bu">print</span> <span class="st">'?'</span>
<span class="co"># for</span>
a <span class="op">=</span> [<span class="st">'red'</span>, <span class="st">'blue'</span>, <span class="st">'green'</span>]
<span class="cf">for</span> color <span class="op">in</span> a:
<span class="bu">print</span> color
<span class="co"># while</span>
number <span class="op">=</span> <span class="dv">1</span>
<span class="cf">while</span> number <span class="op"><</span> <span class="dv">10</span>:
<span class="bu">print</span> number
number <span class="op">+=</span> <span class="dv">1</span>
<span class="co"># break</span>
number <span class="op">=</span> <span class="dv">1</span>
<span class="cf">while</span> <span class="va">True</span>:
<span class="bu">print</span> number
number <span class="op">+=</span> <span class="dv">1</span>
<span class="cf">if</span> number <span class="op">></span> <span class="dv">10</span>:
<span class="cf">break</span>
<span class="co"># continue</span>
<span class="cf">for</span> i <span class="op">in</span> <span class="bu">range</span>(<span class="dv">20</span>):
<span class="cf">if</span> i <span class="op">%</span> <span class="dv">2</span> <span class="op">==</span> <span class="dv">0</span>:
<span class="cf">continue</span>
<span class="bu">print</span> i</code></pre></div>
<h3 id="functions-classes-generators-decorators">Functions, Classes, Generators, Decorators</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="co"># Function</span>
<span class="kw">def</span> myfunc(a1, a2):
<span class="cf">return</span> x
x <span class="op">=</span> my_function(a1,a2)
<span class="co"># Class</span>
<span class="kw">class</span> Point(<span class="bu">object</span>):
<span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, x):
<span class="va">self</span>.x <span class="op">=</span> x
<span class="kw">def</span> <span class="fu">__call__</span>(<span class="va">self</span>):
<span class="bu">print</span> <span class="va">self</span>.x
x <span class="op">=</span> Point(<span class="dv">3</span>)
<span class="co"># Generators</span>
<span class="kw">def</span> firstn(n):
num <span class="op">=</span> <span class="dv">0</span>
<span class="cf">while</span> num <span class="op"><</span> n:
<span class="cf">yield</span> num
num <span class="op">+=</span> <span class="dv">1</span>
x <span class="op">=</span> [<span class="cf">for</span> i <span class="op">in</span> firstn(<span class="dv">10</span>)]
<span class="co"># Decorators</span>
<span class="kw">class</span> myDecorator(<span class="bu">object</span>):
<span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, f):
<span class="va">self</span>.f <span class="op">=</span> f
<span class="kw">def</span> <span class="fu">__call__</span>(<span class="va">self</span>):
<span class="bu">print</span> <span class="st">"call"</span>
<span class="va">self</span>.f()
<span class="at">@myDecorator</span>
<span class="kw">def</span> my_funct():
<span class="bu">print</span> <span class="st">'func'</span>
my_func()</code></pre></div>
<h2 id="numpy">NumPy</h2>
<h3 id="array-initialization">array initialization</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.array([<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">4</span>]) <span class="co"># direct initialization</span>
np.empty(<span class="dv">20</span>, dtype<span class="op">=</span>np.float32) <span class="co"># single precision array with 20 entries</span>
np.zeros(<span class="dv">200</span>) <span class="co"># initialize 200 zeros</span>
np.ones((<span class="dv">3</span>,<span class="dv">3</span>), dtype<span class="op">=</span>np.int32) <span class="co"># 3 x 3 integer matrix with ones</span>
np.eye(<span class="dv">200</span>) <span class="co"># ones on the diagonal</span>
np.zeros_like(a) <span class="co"># returns array with zeros and the shape of a</span>
np.linspace(<span class="dv">0</span>., <span class="dv">10</span>., <span class="dv">100</span>) <span class="co"># 100 points from 0 to 10</span>
np.arange(<span class="dv">0</span>, <span class="dv">100</span>, <span class="dv">2</span>) <span class="co"># points from 0 to <100 with step width 2</span>
np.logspace(<span class="op">-</span><span class="dv">5</span>, <span class="dv">2</span>, <span class="dv">100</span>) <span class="co"># 100 log-spaced points between 1e-5 and 1e2</span>
np.copy(a) <span class="co"># copy array to new memory</span></code></pre></div>
<h3 id="reading-writing-files">reading/ writing files</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.fromfile(fname<span class="op">/</span><span class="bu">object</span>, dtype<span class="op">=</span>np.float32, count<span class="op">=</span><span class="dv">5</span>) <span class="co"># read binary data from file</span>
np.loadtxt(fname<span class="op">/</span><span class="bu">object</span>, skiprows<span class="op">=</span><span class="dv">2</span>, delimiter<span class="op">=</span><span class="st">','</span>) <span class="co"># read ascii data from file</span></code></pre></div>
<h3 id="array-properties-and-operations">array properties and operations</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a.shape <span class="co"># a tuple with the lengths of each axis</span>
<span class="bu">len</span>(a) <span class="co"># length of axis 0</span>
a.ndim <span class="co"># number of dimensions (axes)</span>
a.sort(axis<span class="op">=</span><span class="dv">1</span>) <span class="co"># sort array along axis</span>
a.flatten() <span class="co"># collapse array to one dimension</span>
a.conj() <span class="co"># return complex conjugate</span>
a.astype(np.int16) <span class="co"># cast to integer</span>
np.argmax(a, axis<span class="op">=</span><span class="dv">2</span>) <span class="co"># return index of maximum along a given axis</span>
np.cumsum(a) <span class="co"># return cumulative sum</span>
np.<span class="bu">any</span>(a) <span class="co"># True if any element is True</span>
np.<span class="bu">all</span>(a) <span class="co"># True if all elements are True</span>
np.argsort(a, axis<span class="op">=</span><span class="dv">1</span>) <span class="co"># return sorted index array along axis</span></code></pre></div>
<h3 id="indexing">indexing</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">=</span> np.arange(<span class="dv">100</span>) <span class="co"># initialization with 0 - 99</span>
a[: <span class="dv">3</span>] <span class="op">=</span> <span class="dv">0</span> <span class="co"># set the first three indices to zero</span>
a[<span class="dv">1</span>: <span class="dv">5</span>] <span class="op">=</span> <span class="dv">1</span> <span class="co"># set indices 1-4 to 1</span>
a[start:stop:step] <span class="co"># general form of indexing/slicing</span>
a[<span class="va">None</span>, :] <span class="co"># transform to column vector</span>
a[[<span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">8</span>]] <span class="co"># return array with values of the indices</span>
a <span class="op">=</span> a.reshape(<span class="dv">10</span>, <span class="dv">10</span>) <span class="co"># transform to 10 x 10 matrix</span>
a.T <span class="co"># return transposed view</span>
np.transpose(a, (<span class="dv">2</span>, <span class="dv">1</span>, <span class="dv">0</span>)) <span class="co"># transpose array to new axis order</span>
a[a <span class="op"><</span> <span class="dv">2</span>] <span class="co"># returns array that fulfills elementwise condition</span></code></pre></div>
<h3 id="boolean-arrays">boolean arrays</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op"><</span> <span class="dv">2</span> <span class="co"># returns array with boolean values</span>
np.logical_and(a <span class="op"><</span> <span class="dv">2</span>, b <span class="op">></span> <span class="dv">10</span>) <span class="co"># elementwise logical and</span>
np.logical_or(a <span class="op"><</span> <span class="dv">2</span>, b <span class="op">></span> <span class="dv">10</span>) <span class="co"># elementwise logical or</span>
<span class="op">~</span>a <span class="co"># invert boolean array</span>
np.invert(a) <span class="co"># invert boolean array</span></code></pre></div>
<h3 id="elementwise-operations-and-math-functions">elementwise operations and math functions</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">a <span class="op">*</span> <span class="dv">5</span> <span class="co"># multiplication with scalar</span>
a <span class="op">+</span> <span class="dv">5</span> <span class="co"># addition with scalar</span>
a <span class="op">+</span> b <span class="co"># addition with array b</span>
a <span class="op">/</span> b <span class="co"># division with b (np.NaN for division by zero)</span>
np.exp(a) <span class="co"># exponential (complex and real)</span>
np.sin(a) <span class="co"># sine</span>
np.cos(a) <span class="co"># cosine</span>
np.arctan2(y,x) <span class="co"># arctan(y/x)</span>
np.arcsin(x) <span class="co"># arcsin</span>
np.radians(a) <span class="co"># degrees to radians</span>
np.degrees(a) <span class="co"># radians to degrees</span>
np.var(a) <span class="co"># variance of array</span>
np.std(a, axis<span class="op">=</span><span class="dv">1</span>) <span class="co"># standard deviation</span></code></pre></div>
<h3 id="inner-outer-products">inner / outer products</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.dot(a, b) <span class="co"># inner matrix product: a_mi b_in</span>
np.einsum(<span class="st">'ijkl,klmn->ijmn'</span>, a, b) <span class="co"># einstein summation convention</span>
np.<span class="bu">sum</span>(a, axis<span class="op">=</span><span class="dv">1</span>) <span class="co"># sum over axis 1</span>
np.<span class="bu">abs</span>(a) <span class="co"># return array with absolute values</span>
a[<span class="va">None</span>, :] <span class="op">+</span> b[:, <span class="va">None</span>] <span class="co"># outer sum</span>
a[<span class="va">None</span>, :] <span class="op">*</span> b[<span class="va">None</span>, :] <span class="co"># outer product</span>
np.outer(a, b) <span class="co"># outer product</span>
np.<span class="bu">sum</span>(a <span class="op">*</span> a.T) <span class="co"># matrix norm</span></code></pre></div>
<h3 id="interpolation-integration">interpolation, integration</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.trapz(y, x<span class="op">=</span>x, axis<span class="op">=</span><span class="dv">1</span>) <span class="co"># integrate along axis 1</span>
np.interp(x, xp, yp) <span class="co"># interpolate function xp, yp at points x</span></code></pre></div>
<h3 id="fft">fft</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.fft.fft(y) <span class="co"># complex fourier transform of y</span>
np.fft.fftfreqs(<span class="bu">len</span>(y)) <span class="co"># fft frequencies for a given length</span>
np.fft.fftshift(freqs) <span class="co"># shifts zero frequency to the middle</span>
np.fft.rfft(y) <span class="co"># real fourier transform of y</span>
np.fft.rfftfreqs(<span class="bu">len</span>(y)) <span class="co"># real fft frequencies for a given length</span></code></pre></div>
<h3 id="rounding">rounding</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.ceil(a) <span class="co"># rounds to nearest upper int</span>
np.floor(a) <span class="co"># rounds to nearest lower int</span>
np.<span class="bu">round</span>(a) <span class="co"># rounds to neares int</span></code></pre></div>
<h3 id="random-variables">random variables</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">np.random.normal(loc<span class="op">=</span><span class="dv">0</span>, scale<span class="op">=</span><span class="dv">2</span>, size<span class="op">=</span><span class="dv">100</span>) <span class="co"># 100 normal distributed random numbers</span>
np.random.seed(<span class="dv">23032</span>) <span class="co"># resets the seed value</span>
np.random.rand(<span class="dv">200</span>) <span class="co"># 200 random numbers in [0, 1)</span>
np.random.uniform(<span class="dv">1</span>, <span class="dv">30</span>, <span class="dv">200</span>) <span class="co"># 200 random numbers in [1, 30)</span>
np.random.random_integers(<span class="dv">1</span>, <span class="dv">15</span>, <span class="dv">300</span>) <span class="co"># 300 random integers between [1, 15]</span></code></pre></div>
<h2 id="matplotlib">Matplotlib</h2>
<h3 id="figures-and-axes">figures and axes</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">fig <span class="op">=</span> plt.figure(figsize<span class="op">=</span>(<span class="dv">5</span>, <span class="dv">2</span>), facecolor<span class="op">=</span><span class="st">'black'</span>) <span class="co"># initialize figure</span>
ax <span class="op">=</span> fig.add_subplot(<span class="dv">3</span>, <span class="dv">2</span>, <span class="dv">2</span>) <span class="co"># add second subplot in a 3 x 2 grid</span>
fig, axes <span class="op">=</span> plt.subplots(<span class="dv">5</span>, <span class="dv">2</span>, figsize<span class="op">=</span>(<span class="dv">5</span>, <span class="dv">5</span>)) <span class="co"># return fig and array of axes in a 5 x 2 grid</span>
ax <span class="op">=</span> fig.add_axes([left, bottom, width, height]) <span class="co"># manually add axes at a certain position</span></code></pre></div>
<h3 id="figures-and-axes-properties">figures and axes properties</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">fig.suptitle(<span class="st">'title'</span>) <span class="co"># big figure title</span>
fig.subplots_adjust(bottom<span class="op">=</span><span class="fl">0.1</span>, right<span class="op">=</span><span class="fl">0.8</span>, top<span class="op">=</span><span class="fl">0.9</span>, wspace<span class="op">=</span><span class="fl">0.2</span>,
hspace<span class="op">=</span><span class="fl">0.5</span>) <span class="co"># adjust subplot positions</span>
fig.tight_layout(pad<span class="op">=</span><span class="fl">0.1</span>,h_pad<span class="op">=</span><span class="fl">0.5</span>, w_pad<span class="op">=</span><span class="fl">0.5</span>, rect<span class="op">=</span><span class="va">None</span>) <span class="co"># adjust</span>
subplots to fit perfectly into fig
ax.set_xlabel() <span class="co"># set xlabel</span>
ax.set_ylabel() <span class="co"># set ylabel</span>
ax.set_xlim(<span class="dv">1</span>, <span class="dv">2</span>) <span class="co"># sets x limits</span>
ax.set_ylim(<span class="dv">3</span>, <span class="dv">4</span>) <span class="co"># sets y limits</span>
ax.set_title(<span class="st">'blabla'</span>) <span class="co"># sets the axis title</span>
ax.<span class="bu">set</span>(xlabel<span class="op">=</span><span class="st">'bla'</span>) <span class="co"># set multiple parameters at once</span>
ax.legend(loc<span class="op">=</span><span class="st">'upper center'</span>) <span class="co"># activate legend</span>
ax.grid(<span class="va">True</span>, which<span class="op">=</span><span class="st">'both'</span>) <span class="co"># activate grid</span>
bbox <span class="op">=</span> ax.get_position() <span class="co"># returns the axes bounding box</span>
bbox.x0 <span class="op">+</span> bbox.width <span class="co"># bounding box parameters</span></code></pre></div>
<h3 id="plotting-routines">plotting routines</h3>
<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">ax.plot(x,y, <span class="st">'-o'</span>, c<span class="op">=</span><span class="st">'red'</span>, lw<span class="op">=</span><span class="dv">2</span>, label<span class="op">=</span><span class="st">'bla'</span>) <span class="co"># plots a line</span>
ax.scatter(x,y, s<span class="op">=</span><span class="dv">20</span>, c<span class="op">=</span>color) <span class="co"># scatter plot</span>
ax.pcolormesh(xx,yy,zz, shading<span class="op">=</span><span class="st">'gouraud'</span>) <span class="co"># fast colormesh function</span>
ax.colormesh(xx,yy,zz, norm<span class="op">=</span>norm) <span class="co"># slower colormesh function</span>
ax.contour(xx,yy,zz, cmap<span class="op">=</span><span class="st">'jet'</span>) <span class="co"># contour line plot</span>
ax.contourf(xx,yy,zz, vmin<span class="op">=</span><span class="dv">2</span>, vmax<span class="op">=</span><span class="dv">4</span>) <span class="co"># filled contours plot</span>
n, bins, patch <span class="op">=</span> ax.hist(x, <span class="dv">50</span>) <span class="co"># histogram</span>
ax.imshow(matrix, origin<span class="op">=</span><span class="st">'lower'</span>, extent<span class="op">=</span>(x1, x2, y1, y2)) <span class="co"># show image</span>
ax.specgram(y, FS<span class="op">=</span><span class="fl">0.1</span>, noverlap<span class="op">=</span><span class="dv">128</span>, scale<span class="op">=</span><span class="st">'linear'</span>) <span class="co"># plot a spectrogram</span></code></pre></div>
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