NumPy

gto76 · gto76 · commit 0f2ae68ac808 · 2023-04-02T14:59:55.000+02:00
diff --git a/README.md b/README.md
@@ -2662,14 +2662,14 @@ import numpy as np
 
 ```python
 <array> = np.copy/abs/sqrt/log/int64(<array>)           # Returns new array of the same shape.
-<array> = <array>.sum/max/mean/argmax/all([axis])       # Passed dimension gets aggregated.
+<array> = <array>.sum/max/mean/argmax/all(axis)         # Passed dimension gets aggregated.
 <array> = np.apply_along_axis(<func>, axis, <array>)    # Func can return a scalar or array.
 ```
 
 ```python
 <array> = np.concatenate(<list_of_arrays>, axis=0)      # Links arrays along first axis (rows).
 <array> = np.row_stack/column_stack(<list_of_arrays>)   # Treats 1d arrays as rows or columns.
-<array> = np.tile(<array>, <int/shape>)                 # Multiplies passed array.
+<array> = np.tile/repeat(<array>, <int/list>)           # Tiles array or repeats its elements.
 ```
 * **Shape is a tuple of dimension sizes. A 100x50 RGB image has shape (50, 100, 3).**
 * **Axis is an index of the dimension that gets aggregated. Leftmost dimension has index 0. Summing the RGB image along axis 2 will return a greyscale image with shape (50, 100).**
diff --git a/index.html b/index.html
@@ -54,7 +54,7 @@
 
 <body>
   <header>
-    <aside>April 1, 2023</aside>
+    <aside>April 2, 2023</aside>
     <a href="https://gto76.github.io" rel="author">Jure Šorn</a>
   </header>
 
@@ -2179,12 +2179,12 @@ <h3 id="format-2">Format</h3><div><h4 id="forstandardtypesizesandmanualalignment
 &lt;view&gt;  = &lt;array&gt;.transpose()                           <span class="hljs-comment"># Or: &lt;array&gt;.T</span>
 </code></pre>
 <pre><code class="python language-python hljs">&lt;array&gt; = np.copy/abs/sqrt/log/int64(&lt;array&gt;)           <span class="hljs-comment"># Returns new array of the same shape.</span>
-&lt;array&gt; = &lt;array&gt;.sum/max/mean/argmax/all([axis])       <span class="hljs-comment"># Passed dimension gets aggregated.</span>
+&lt;array&gt; = &lt;array&gt;.sum/max/mean/argmax/all(axis)         <span class="hljs-comment"># Passed dimension gets aggregated.</span>
 &lt;array&gt; = np.apply_along_axis(&lt;func&gt;, axis, &lt;array&gt;)    <span class="hljs-comment"># Func can return a scalar or array.</span>
 </code></pre>
 <pre><code class="python language-python hljs">&lt;array&gt; = np.concatenate(&lt;list_of_arrays&gt;, axis=<span class="hljs-number">0</span>)      <span class="hljs-comment"># Links arrays along first axis (rows).</span>
 &lt;array&gt; = np.row_stack/column_stack(&lt;list_of_arrays&gt;)   <span class="hljs-comment"># Treats 1d arrays as rows or columns.</span>
-&lt;array&gt; = np.tile(&lt;array&gt;, &lt;int/shape&gt;)                 <span class="hljs-comment"># Multiplies passed array.</span>
+&lt;array&gt; = np.tile/repeat(&lt;array&gt;, &lt;int/list&gt;)           <span class="hljs-comment"># Tiles array or repeats its elements.</span>
 </code></pre>
 <ul>
 <li><strong>Shape is a tuple of dimension sizes. A 100x50 RGB image has shape (50, 100, 3).</strong></li>
@@ -2935,7 +2935,7 @@ <h3 id="format-2">Format</h3><div><h4 id="forstandardtypesizesandmanualalignment
  
 
   <footer>
-    <aside>April 1, 2023</aside>
+    <aside>April 2, 2023</aside>
     <a href="https://gto76.github.io" rel="author">Jure Šorn</a>
   </footer>
 
