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NumPy

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Jure Šorn 4 months ago
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  1. 11
      README.md
  2. 19
      index.html

11
README.md
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@ -2710,7 +2710,7 @@ import numpy as np
<1/2d_arr> = <2d>[<2d/1d_bools>] # 1d_bools must have size of a column.
```
* **`':'` returns a slice of all dimension's indices. Omitted dimensions default to `':'`.**
* **Sixth line fails if tuple is used because Python converts `'obj[i, j]'` to `'obj[(i, j)]'`!**
* **Python converts `'obj[i, j]'` to `'obj[(i, j)]'`. This makes `'<2d>[row_i, col_i]'` and `'<2d>[row_indices]'` indistinguishable to NumPy if tuple of indices is passed!**
* **Indexing with a slice and 1d array works the same as when using two slices (lines 4, 6, 7).**
* **`'ix_([1, 2], [3, 4])'` returns `'[[1], [2]]'` and `'[[3, 4]]'`. Due to broadcasting rules, this is the same as using `'[[1, 1], [2, 2]]'` and `'[[3, 4], [3, 4]]'`.**
* **Any value that is broadcastable to the indexed shape can be assigned to the selection.**
@ -2734,7 +2734,9 @@ right = [[0.1], [0.6], [0.8]] # Shape: (3, 1)
left = [[0.1, 0.6, 0.8], # Shape: (3, 3) <- !
[0.1, 0.6, 0.8],
[0.1, 0.6, 0.8]]
```
```python
right = [[0.1, 0.1, 0.1], # Shape: (3, 3) <- !
[0.6, 0.6, 0.6],
[0.8, 0.8, 0.8]]
@ -2748,14 +2750,11 @@ right = [[0.1, 0.1, 0.1], # Shape: (3, 3) <- !
[ 0.1, 0.6, 0.8 ]
>>> wrapped_points = points.reshape(3, 1)
[[0.1], [0.6], [0.8]]
>>> distances = points - wrapped_points
>>> deltas = points - wrapped_points
[[ 0. , 0.5, 0.7],
[-0.5, 0. , 0.2],
[-0.7, -0.2, 0. ]]
>>> distances = np.abs(distances)
[[ 0. , 0.5, 0.7],
[ 0.5, 0. , 0.2],
[ 0.7, 0.2, 0. ]]
>>> distances = np.abs(deltas)
>>> distances[range(3), range(3)] = np.inf
[[ inf, 0.5, 0.7],
[ 0.5, inf, 0.2],

19
index.html
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@ -55,7 +55,7 @@
<body>
<header>
<aside>December 11, 2024</aside>
<aside>December 13, 2024</aside>
<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
</header>
@ -2211,7 +2211,7 @@ $ snakeviz test.prof <span class="hlj
</code></pre>
<ul>
<li><strong><code class="python hljs"><span class="hljs-string">':'</span></code> returns a slice of all dimension's indices. Omitted dimensions default to <code class="python hljs"><span class="hljs-string">':'</span></code>.</strong></li>
<li><strong>Sixth line fails if tuple is used because Python converts <code class="python hljs"><span class="hljs-string">'obj[i, j]'</span></code> to <code class="python hljs"><span class="hljs-string">'obj[(i, j)]'</span></code>!</strong></li>
<li><strong>Python converts <code class="python hljs"><span class="hljs-string">'obj[i, j]'</span></code> to <code class="python hljs"><span class="hljs-string">'obj[(i, j)]'</span></code>. This makes <code class="python hljs"><span class="hljs-string">'&lt;2d&gt;[row_i, col_i]'</span></code> and <code class="python hljs"><span class="hljs-string">'&lt;2d&gt;[row_indices]'</span></code> indistinguishable to NumPy if tuple of indices is passed!</strong></li>
<li><strong>Indexing with a slice and 1d array works the same as when using two slices (lines 4, 6, 7).</strong></li>
<li><strong><code class="python hljs"><span class="hljs-string">'ix_([1, 2], [3, 4])'</span></code> returns <code class="python hljs"><span class="hljs-string">'[[1], [2]]'</span></code> and <code class="python hljs"><span class="hljs-string">'[[3, 4]]'</span></code>. Due to broadcasting rules, this is the same as using <code class="python hljs"><span class="hljs-string">'[[1, 1], [2, 2]]'</span></code> and <code class="python hljs"><span class="hljs-string">'[[3, 4], [3, 4]]'</span></code>.</strong></li>
<li><strong>Any value that is broadcastable to the indexed shape can be assigned to the selection.</strong></li>
@ -2228,24 +2228,21 @@ right = [[<span class="hljs-number">0.1</span>], [<span class="hljs-number">0.6<
<div><h4 id="2ifanydimensionsdifferinsizeexpandtheonesthathavesize1byduplicatingtheirelements">2. If any dimensions differ in size, expand the ones that have size 1 by duplicating their elements:</h4><pre><code class="python language-python hljs">left = [[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.8</span>], <span class="hljs-comment"># Shape: (3, 3) &lt;- !</span>
[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.8</span>],
[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.8</span>]]
</code></pre></div>
right = [[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.1</span>, <span class="hljs-number">0.1</span>], <span class="hljs-comment"># Shape: (3, 3) &lt;- !</span>
<pre><code class="python language-python hljs">right = [[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.1</span>, <span class="hljs-number">0.1</span>], <span class="hljs-comment"># Shape: (3, 3) &lt;- !</span>
[<span class="hljs-number">0.6</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.6</span>],
[<span class="hljs-number">0.8</span>, <span class="hljs-number">0.8</span>, <span class="hljs-number">0.8</span>]]
</code></pre></div>
</code></pre>
<div><h3 id="example-3">Example</h3><div><h4 id="foreachpointreturnsindexofitsnearestpoint010608121">For each point returns index of its nearest point (<code class="python hljs">[<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.8</span>] =&gt; [<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">1</span>]</code>):</h4><pre><code class="python language-python hljs"><span class="hljs-meta">&gt;&gt;&gt; </span>points = np.array([<span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.8</span>])
[ <span class="hljs-number">0.1</span>, <span class="hljs-number">0.6</span>, <span class="hljs-number">0.8</span> ]
<span class="hljs-meta">&gt;&gt;&gt; </span>wrapped_points = points.reshape(<span class="hljs-number">3</span>, <span class="hljs-number">1</span>)
[[<span class="hljs-number">0.1</span>], [<span class="hljs-number">0.6</span>], [<span class="hljs-number">0.8</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>distances = points - wrapped_points
<span class="hljs-meta">&gt;&gt;&gt; </span>deltas = points - wrapped_points
[[ <span class="hljs-number">0.</span> , <span class="hljs-number">0.5</span>, <span class="hljs-number">0.7</span>],
[<span class="hljs-number">-0.5</span>, <span class="hljs-number">0.</span> , <span class="hljs-number">0.2</span>],
[<span class="hljs-number">-0.7</span>, <span class="hljs-number">-0.2</span>, <span class="hljs-number">0.</span> ]]
<span class="hljs-meta">&gt;&gt;&gt; </span>distances = np.abs(distances)
[[ <span class="hljs-number">0.</span> , <span class="hljs-number">0.5</span>, <span class="hljs-number">0.7</span>],
[ <span class="hljs-number">0.5</span>, <span class="hljs-number">0.</span> , <span class="hljs-number">0.2</span>],
[ <span class="hljs-number">0.7</span>, <span class="hljs-number">0.2</span>, <span class="hljs-number">0.</span> ]]
<span class="hljs-meta">&gt;&gt;&gt; </span>distances = np.abs(deltas)
<span class="hljs-meta">&gt;&gt;&gt; </span>distances[range(<span class="hljs-number">3</span>), range(<span class="hljs-number">3</span>)] = np.inf
[[ inf, <span class="hljs-number">0.5</span>, <span class="hljs-number">0.7</span>],
[ <span class="hljs-number">0.5</span>, inf, <span class="hljs-number">0.2</span>],
@ -2925,7 +2922,7 @@ $ deactivate <span class="hljs-comment"># Deactivates the active
<footer>
<aside>December 11, 2024</aside>
<aside>December 13, 2024</aside>
<a href="https://gto76.github.io" rel="author">Jure Šorn</a>
</footer>

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