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SimonMolinsky merged 2 commits into from
Feb 22, 2026
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518 distanceblockpy calculate block to block distance add reference equation to the documentation #519

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df42021
Updated equations in documentation
SimonMolinsky Feb 22, 2026
5192e66
Update block.py
SimonMolinsky Feb 22, 2026
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2 changes: 1 addition & 1 deletion CHANGELOG.rst
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Expand Up @@ -8,7 +8,7 @@ Changes - from version >= 1.x

* [experimental] LSA-method Ordinary Kriging tests and experiments
* [enhancement] Users can set negative predictions to zero in Area-to-Area, Area-to-Point, and Centroid-based Poisson Kriging

* [docs] Added block-to-block distance equation, and corrected semivariance equation in functions docstrings

2025-12-26
----------
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48 changes: 37 additions & 11 deletions docs/build/html/_modules/pyinterpolate/core/pipelines/block_filter.html
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27 changes: 24 additions & 3 deletions docs/build/html/_modules/pyinterpolate/distance/block.html
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>pyinterpolate.distance.block &#8212; pyinterpolate 1.1.0 documentation</title>
<title>pyinterpolate.distance.block &#8212; pyinterpolate 1.2.0 documentation</title>



Expand Down Expand Up @@ -38,7 +38,7 @@
<link rel="preload" as="script" href="../../../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
<link rel="preload" as="script" href="../../../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />

<script src="../../../_static/documentation_options.js?v=fc837d61"></script>
<script src="../../../_static/documentation_options.js?v=6efca38a"></script>
<script src="../../../_static/doctools.js?v=9bcbadda"></script>
<script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
Expand Down Expand Up @@ -111,7 +111,7 @@



<p class="title logo__title">pyinterpolate 1.1.0 documentation</p>
<p class="title logo__title">pyinterpolate 1.2.0 documentation</p>

</a></div>

Expand Down Expand Up @@ -639,6 +639,27 @@ <h1>Source code for pyinterpolate.distance.block</h1><div class="highlight"><pre
<span class="sd"> AttributeError</span>
<span class="sd"> Blocks are provided as DataFrame but column names were not given.</span>

<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> The weighted distance between blocks is derived from the equation 3 given</span>
<span class="sd"> in publication [1] from References.</span>

<span class="sd"> .. math:: d(v_{a}, v_{b})=\frac{1}{\sum_{s=1}^{P_{a}} \sum_{s&#39;=1}^{P_{b}} n(u_{s}) n(u_{s&#39;})} * \sum_{s=1}^{P_{a}} \sum_{s&#39;=1}^{P_{b}} n(u_{s})n(u_{s&#39;})||u_{s}-u_{s&#39;}||</span>

<span class="sd"> where:</span>
<span class="sd"> * :math:`P_{a}` and :math:`P_{b}`: number of points :math:`u_{s}`</span>
<span class="sd"> and :math:`u_{s&#39;}` used to discretize the two units :math:`v_{a}`</span>
<span class="sd"> and :math:`v_{b}`</span>
<span class="sd"> * :math:`n(u_{s})` and :math:`n(u_{s&#39;})` - population size in</span>
<span class="sd"> the cells :math:`u_{s}` and :math:`u_{s&#39;}`</span>

<span class="sd"> References</span>
<span class="sd"> ----------</span>
<span class="sd"> .. [1] Goovaerts, P. Kriging and Semivariogram Deconvolution in the</span>
<span class="sd"> Presence of Irregular Geographical Units.</span>
<span class="sd"> Math Geosci 40, 101–128 (2008).</span>
<span class="sd"> https://doi.org/10.1007/s11004-007-9129-1</span>

<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; import os</span>
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14 changes: 10 additions & 4 deletions docs/build/html/_modules/pyinterpolate/kriging/block/area_to_area_poisson_kriging.html
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>pyinterpolate.kriging.block.area_to_area_poisson_kriging &#8212; pyinterpolate 1.1.0 documentation</title>
<title>pyinterpolate.kriging.block.area_to_area_poisson_kriging &#8212; pyinterpolate 1.2.0 documentation</title>



Expand Down Expand Up @@ -38,7 +38,7 @@
<link rel="preload" as="script" href="../../../../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
<link rel="preload" as="script" href="../../../../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />

<script src="../../../../_static/documentation_options.js?v=fc837d61"></script>
<script src="../../../../_static/documentation_options.js?v=6efca38a"></script>
<script src="../../../../_static/doctools.js?v=9bcbadda"></script>
<script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
Expand Down Expand Up @@ -111,7 +111,7 @@



<p class="title logo__title">pyinterpolate 1.1.0 documentation</p>
<p class="title logo__title">pyinterpolate 1.2.0 documentation</p>

</a></div>

Expand Down Expand Up @@ -450,7 +450,8 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_area_poisson_kriging</h1
<span class="n">number_of_neighbors</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">neighbors_range</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">raise_when_negative_prediction</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">raise_when_negative_error</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
<span class="n">raise_when_negative_error</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">negative_prediction_to_zero</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function predicts areal value in an unknown location based on</span>
<span class="sd"> the area-to-area Poisson Kriging</span>
Expand Down Expand Up @@ -480,6 +481,9 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_area_poisson_kriging</h1
<span class="sd"> raise_when_negative_error : bool, default=True</span>
<span class="sd"> Raise error when prediction error is negative.</span>

<span class="sd"> negative_prediction_to_zero : bool, default=False</span>
<span class="sd"> While prediction is negative, then set it to 0.</span>

<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> results : Dict</span>
Expand Down Expand Up @@ -639,6 +643,8 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_area_poisson_kriging</h1
<span class="sa">f</span><span class="s1">&#39;not be lower than 0. Check your sampling &#39;</span>
<span class="sa">f</span><span class="s1">&#39;grid, samples, number of neighbors or &#39;</span>
<span class="sa">f</span><span class="s1">&#39;semivariogram model type.&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">negative_prediction_to_zero</span><span class="p">:</span>
<span class="n">zhat</span> <span class="o">=</span> <span class="mi">0</span>

<span class="n">sigmasq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">covars</span><span class="p">)</span>

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64 changes: 37 additions & 27 deletions docs/build/html/_modules/pyinterpolate/kriging/block/area_to_point_poisson_kriging.html
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>pyinterpolate.kriging.block.area_to_point_poisson_kriging &#8212; pyinterpolate 1.1.0 documentation</title>
<title>pyinterpolate.kriging.block.area_to_point_poisson_kriging &#8212; pyinterpolate 1.2.0 documentation</title>



Expand Down Expand Up @@ -38,7 +38,7 @@
<link rel="preload" as="script" href="../../../../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
<link rel="preload" as="script" href="../../../../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />

<script src="../../../../_static/documentation_options.js?v=fc837d61"></script>
<script src="../../../../_static/documentation_options.js?v=6efca38a"></script>
<script src="../../../../_static/doctools.js?v=9bcbadda"></script>
<script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
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<p class="title logo__title">pyinterpolate 1.1.0 documentation</p>
<p class="title logo__title">pyinterpolate 1.2.0 documentation</p>

</a></div>

Expand Down Expand Up @@ -437,8 +437,10 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_point_poisson_kriging</h
<span class="kn">from</span> <span class="nn">pyinterpolate.kriging.block.weights</span> <span class="kn">import</span> <span class="n">pk_weights_array</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.semivariogram.deconvolution.block_to_block_semivariance</span> <span class="kn">import</span> \
<span class="n">weighted_avg_point_support_semivariances</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.semivariogram.theoretical.classes.theoretical_variogram</span> <span class="kn">import</span> <span class="n">TheoreticalVariogram</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.transform.select_poisson_kriging_data</span> <span class="kn">import</span> <span class="n">select_poisson_kriging_data</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.semivariogram.theoretical.classes.theoretical_variogram</span> <span class="kn">import</span> \
<span class="n">TheoreticalVariogram</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.transform.select_poisson_kriging_data</span> <span class="kn">import</span> \
<span class="n">select_poisson_kriging_data</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.transform.statistical</span> <span class="kn">import</span> <span class="n">sem_to_cov</span>
<span class="kn">from</span> <span class="nn">pyinterpolate.transform.transform</span> <span class="kn">import</span> <span class="n">add_ones</span>

Expand All @@ -452,7 +454,8 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_point_poisson_kriging</h
<span class="n">neighbors_range</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">raise_when_negative_prediction</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">raise_when_negative_error</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">err_to_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="n">err_to_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">negative_prediction_to_zero</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function predicts point-support value in the unknown location based on</span>
<span class="sd"> the area-to-point Poisson Kriging</span>
Expand Down Expand Up @@ -486,6 +489,9 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_point_poisson_kriging</h
<span class="sd"> When point interpolation returns ``ValueError`` then set prediction</span>
<span class="sd"> or variance error to ``NaN``.</span>

<span class="sd"> negative_prediction_to_zero : bool, default=False</span>
<span class="sd"> While prediction is negative, then set it to 0.</span>

<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> results : Dict[numpy array]</span>
Expand Down Expand Up @@ -687,16 +693,21 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_point_poisson_kriging</h

<span class="k">if</span> <span class="n">zhat</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">if</span> <span class="n">raise_when_negative_prediction</span><span class="p">:</span>
<span class="k">if</span> <span class="n">err_to_nan</span><span class="p">:</span>
<span class="n">predicted_points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="p">[</span><span class="n">upoint</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upoint</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">continue</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicted value is </span><span class="si">{</span><span class="n">zhat</span><span class="si">}</span><span class="s1"> and it &#39;</span>
<span class="sa">f</span><span class="s1">&#39;should not be lower than 0. Check your &#39;</span>
<span class="sa">f</span><span class="s1">&#39;sampling grid, samples, number of &#39;</span>
<span class="sa">f</span><span class="s1">&#39;neighbors or semivariogram model type.&#39;</span><span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicted value is </span><span class="si">{</span><span class="n">zhat</span><span class="si">}</span><span class="s1"> and it &#39;</span>
<span class="sa">f</span><span class="s1">&#39;should not be lower than 0. Check your &#39;</span>
<span class="sa">f</span><span class="s1">&#39;sampling grid, samples, number of &#39;</span>
<span class="sa">f</span><span class="s1">&#39;neighbors or semivariogram model type.&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">err_to_nan</span><span class="p">:</span>
<span class="n">predicted_points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="p">[</span><span class="n">upoint</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upoint</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">continue</span>

<span class="k">if</span> <span class="n">negative_prediction_to_zero</span><span class="p">:</span>
<span class="n">predicted_points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="p">[</span><span class="n">upoint</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upoint</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">continue</span>

<span class="n">point_pop</span> <span class="o">=</span> <span class="n">upoint</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">zhat</span> <span class="o">=</span> <span class="p">(</span><span class="n">zhat</span> <span class="o">*</span> <span class="n">point_pop</span><span class="p">)</span> <span class="o">/</span> <span class="n">tot_unknown_value</span>
Expand All @@ -708,17 +719,16 @@ <h1>Source code for pyinterpolate.kriging.block.area_to_point_poisson_kriging</h

<span class="k">if</span> <span class="n">sigmasq</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">if</span> <span class="n">raise_when_negative_error</span><span class="p">:</span>
<span class="k">if</span> <span class="n">err_to_nan</span><span class="p">:</span>
<span class="n">predicted_points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="p">[</span><span class="n">upoint</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upoint</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">zhat</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">continue</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicted error value is </span><span class="si">{</span><span class="n">sigmasq</span><span class="si">}</span><span class="s1"> and &#39;</span>
<span class="sa">f</span><span class="s1">&#39;it should not be lower than 0. &#39;</span>
<span class="sa">f</span><span class="s1">&#39;Check your sampling grid, samples, &#39;</span>
<span class="sa">f</span><span class="s1">&#39;number of neighbors or semivariogram &#39;</span>
<span class="sa">f</span><span class="s1">&#39;model type.&#39;</span><span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicted error value is </span><span class="si">{</span><span class="n">sigmasq</span><span class="si">}</span><span class="s1"> and &#39;</span>
<span class="sa">f</span><span class="s1">&#39;it should not be lower than 0. &#39;</span>
<span class="sa">f</span><span class="s1">&#39;Check your sampling grid, samples, &#39;</span>
<span class="sa">f</span><span class="s1">&#39;number of neighbors or semivariogram &#39;</span>
<span class="sa">f</span><span class="s1">&#39;model type.&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">err_to_nan</span><span class="p">:</span>
<span class="n">predicted_points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="p">[</span><span class="n">upoint</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upoint</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">zhat</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">continue</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
Expand Down
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