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Code Issues 3 Releases Wiki Activity

added an article

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added an article about filtering points that are too far away
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Guillaume 2019-12-01 20:02:38 +01:00
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commit 39100863f8
117 changed files with 423 additions and 110 deletions
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blog/Statistics_10days-day0.html
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@ -185,7 +185,7 @@ For a continuous probability distribution, the median is the value such that a n
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blog/Statistics_10days-day3.html
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@ -256,7 +256,7 @@ we call B the event "a blue ball is drawn" and R the event "a red ball is drawn"
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@ -331,7 +331,7 @@ Between 20 and 22 hours?</p>
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@ -270,7 +270,7 @@ The total number of tickets bought follows a normal distribution of mean <span c
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@ -277,7 +277,7 @@ $$</div>
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cryptocurrencies/Installing_bitcoind_on_raspberry_pi.html
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@ -286,7 +286,7 @@ maxuploadtarget=5000
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dl/csv_logger_keras.html
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@ -279,7 +279,7 @@ Let's first import some libraries</p>
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dl/keras_load.html
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dl/model_checkpoint_keras.html
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@ -235,7 +235,7 @@ Let's first import some libraries</p>
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index.html
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</div>
<div class="panel-body">
<ul>
<li><a href="./python/points_too_far_away.html">Get min and max distance withing a point cloud</a></li>
<li><a href="./python/load_image_PIL.html">Open an image with PIL</a></li>
<li><a href="./python/point_cloud_distance.html">Get min and max distance withing a point cloud</a></li>
<li><a href="./python/numpy_save.html">Save a numpy array to disk</a></li>
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linux/Reloading_.bashrc.html
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@ -144,7 +144,7 @@ However, you can use the following command to do it </p>
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linux/Reloading_fstab.html
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linux/bashrc.html
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linux/free_disk_space_linux.html
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@ -167,7 +167,7 @@ none 0 0 0 - /config
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linux/get_ip_linux.html
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@ -256,7 +256,7 @@ wifi0 Link encap:UNSPEC HWaddr 5C-51-4F-41-7A-AD-00-00-00-00-00-00-00-00-00
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linux/linux_process_information.html
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@ -172,7 +172,7 @@ USER;PID;%CPU;%MEM;VSZ;RSS;TTY;STAT;START;TIME;COMMAND</p>
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linux/md5_recursif.html
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@ -143,7 +143,7 @@ find -type f <span class="se">\(</span> -not -name <span class="s2">&quot;md5sum
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@ -169,7 +169,7 @@ dd <span class="k">if</span><span class="o">=</span>/dev/zero <span class="nv">o
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@ -208,7 +208,7 @@ The secret number is 126
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<h1>
Get min and max distance withing a point cloud
</h1>
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<time class="published" datetime="2019-12-01T19:35:00+01:00">
01 décembre 2019
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<p>In this article we will see how to filter all the point whose distance to any point in an ensemble of points is greater than a specified value. For example, we have two set of points :
* the source in orange
* the target in blue</p>
<p>And we want to find all the points in the target ensemble that are at most at a distance of 0.5 of any point in the source distribution.</p>
<div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">source</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">source</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">source</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;orange&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">target</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">target</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<p><img alt="png" src="../images/points_too_far_away/output_1_0.png"></p>
<p>In order to do so, we will use the <code>cdist</code> function form the <code>scipy.spatial.distance</code> package. This function computes the distance between each pair of the two collections of points. </p>
<p>We compute the minimum distance form all the points in the target ensemble from any point in the source ensemble.</p>
<div class="highlight"><pre><span></span><span class="n">dist</span> <span class="o">=</span> <span class="n">cdist</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
<p>Once the distance has been computed, we filter out all the points that have more distant that the threshold value.</p>
<div class="highlight"><pre><span></span><span class="n">dist</span><span class="p">[</span><span class="n">dist</span><span class="o">&gt;</span><span class="n">thres</span><span class="p">]</span> <span class="o">=</span> <span class="bp">False</span>
<span class="n">dist</span><span class="p">[</span><span class="n">dist</span> <span class="o">!=</span> <span class="bp">False</span><span class="p">]</span> <span class="o">=</span> <span class="bp">True</span>
</pre></div>
<p>after that, we only need to format the result array and filter out all the zero values.</p>
<div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">target</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">,</span> <span class="n">target</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
<span class="k">return</span> <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="p">(</span><span class="n">a</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">cdist</span>
<span class="k">def</span> <span class="nf">filter_too_far</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">thres</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Filters out all the points in the target array </span>
<span class="sd"> whose distance to any point in the source array </span>
<span class="sd"> is greater than the threshold value</span>
<span class="sd"> This function is made for 2D points</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">cdist</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">dist</span><span class="p">[</span><span class="n">dist</span><span class="o">&gt;</span><span class="n">thres</span><span class="p">]</span> <span class="o">=</span> <span class="bp">False</span>
<span class="n">dist</span><span class="p">[</span><span class="n">dist</span> <span class="o">!=</span> <span class="bp">False</span><span class="p">]</span> <span class="o">=</span> <span class="bp">True</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">target</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">,</span> <span class="n">target</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
<span class="k">return</span> <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="p">(</span><span class="n">a</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
</pre></div>
<p>Here we have in green all the point in the target ensemble that are distant from any point in the source ensemble of at most 0.5 units</p>
<div class="highlight"><pre><span></span><span class="n">filtered</span> <span class="o">=</span> <span class="n">filter_too_far</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">thres</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">source</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">source</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;orange&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">target</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">target</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span> <span class="s2">&quot;+&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">filtered</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">filtered</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span> <span class="s2">&quot;+&quot;</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="p">.</span><span class="n">collections</span><span class="p">.</span><span class="n">PathCollection</span> <span class="k">at</span> <span class="mi">0</span><span class="n">x7fde375a4490</span><span class="o">&gt;</span>
</pre></div>
<p><img alt="png" src="../images/points_too_far_away/output_5_1.png"></p>
<p>This function can easily be adapted to work for 3D-points</p>
<div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">cdist</span>
<span class="k">def</span> <span class="nf">filter_too_far</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">thres</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Filters out all the points in the target array </span>
<span class="sd"> whose distance to any point in the source array </span>
<span class="sd"> is greater than the threshold value</span>
<span class="sd"> This function is made for 3D points</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">cdist</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">dist</span><span class="p">[</span><span class="n">dist</span><span class="o">&gt;</span><span class="n">thres</span><span class="p">]</span> <span class="o">=</span> <span class="bp">False</span>
<span class="n">dist</span><span class="p">[</span><span class="n">dist</span> <span class="o">!=</span> <span class="bp">False</span><span class="p">]</span> <span class="o">=</span> <span class="bp">True</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">target</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">,</span> <span class="n">target</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">,</span> <span class="n">target</span><span class="p">[:,</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">dist</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
<span class="k">return</span> <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="p">(</span><span class="n">a</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
</pre></div>
<p>Note that you are not restricted to the euclidian distance, the cdist function can use the following distances:
* braycurtis
* canberra
* chebyshev
* cityblock
* correlation
* cosine
* dice
* euclidean
* hamming
* jaccard
* jensenshannon
* kulsinski
* mahalanobis
* matching
* minkowski
* rogerstanimoto
* russellrao
* euclidean
* sokalmichener
* sokalsneath
* sqeuclidean
* wminkowski
* yule</p>
<div class="highlight"><pre><span></span>
</pre></div>
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