Merge pull request #180 from DataverseLabs/ethmtrgt-fix-points-values

Fixed name of the attribute within the TheoreticalSemivariogram class

changelog.rst

@@ -9,9 +9,20 @@ Changes by date
 2021-XX-XX
 ----------
 
-**version 0.2.3.XX**
+**version 0.2.XX**
+
+* `self.points_values` chenged to `self.points_array` in `TheoreticalSemivariogram` class,
+* `NaN` values are tested and checked in `calc_semivariance_from_pt_cloud()` function,
+
+
+2021-08-23
+----------
+
+**version 0.2.3.post1**
 
 * the outliers removal function: you can choose side for outlier detection and remove. Default is top, available are: both, top, down,
+* the outliers removal function: changed algorithm,
+* new tutorial about outliers and their influence on the final model.
 
 2021-05-13
 ----------

docs/old/build/html/_modules/pyinterpolate/semivariance/semivariogram_fit/fit_semivariance.html

@@ -80,7 +80,7 @@ <h1>Source code for pyinterpolate.semivariance.semivariogram_fit.fit_semivarianc
 <span class="sd">        :param verbose: (bool) if True then all messages are printed, otherwise nothing.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">points_values</span> <span class="o">=</span> <span class="n">points_array</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">points_array</span> <span class="o">=</span> <span class="n">points_array</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">empirical_semivariance</span> <span class="o">=</span> <span class="n">empirical_semivariance</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">theoretical_model</span> <span class="o">=</span> <span class="kc">None</span>
@@ -200,7 +200,7 @@ <h1>Source code for pyinterpolate.semivariance.semivariogram_fit.fit_semivarianc
         <span class="bp">self</span><span class="o">.</span><span class="n">theoretical_model</span> <span class="o">=</span> <span class="n">model</span>
 
         <span class="c1"># sill</span>
-        <span class="n">sill</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">points_values</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">sill</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">points_array</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
 
         <span class="c1"># nugget</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">empirical_semivariance</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
@@ -250,7 +250,7 @@ <h1>Source code for pyinterpolate.semivariance.semivariogram_fit.fit_semivarianc
         <span class="n">base_error</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculate_base_error</span><span class="p">()</span>
 
         <span class="c1"># sill</span>
-        <span class="n">sill</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">points_values</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">sill</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">points_array</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
 
         <span class="c1"># nugget</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">empirical_semivariance</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>

pyinterpolate/semivariance/semivariogram_estimation/calculate_semivariance.py

@@ -243,10 +243,19 @@ def calc_semivariance_from_pt_cloud(pt_cloud_dict):
     experimental_semivariogram = []
     for k in pt_cloud_dict.keys():
         try:
-            experimental_semivariogram.append([k, np.mean(pt_cloud_dict[k]), len(pt_cloud_dict[k])])
+            mean_semivariance_value = np.mean(pt_cloud_dict[k])
+            length = len(pt_cloud_dict[k])
         except ZeroDivisionError:
             # There are no points for this lag
-            experimental_semivariogram.append([k, 0, 0])
+            mean_semivariance_value = 0
+            length = 0
+
+        # Check if any nan
+        if np.isnan(mean_semivariance_value):
+            mean_semivariance_value = 0
+            length = 0
+
+        experimental_semivariogram.append([k, mean_semivariance_value, length])
     experimental_semivariogram = np.array(experimental_semivariogram)
     return experimental_semivariogram
 

pyinterpolate/semivariance/semivariogram_fit/fit_semivariance.py

@@ -1,3 +1,15 @@
+"""
+Authors:
+
+Szymon Molinski | @szymon-datalions
+
+Contributors:
+
+Ethem Turgut | https://github.com/ethmtrgt
+
+"""
+
+
 import csv
 from operator import itemgetter
 import warnings
@@ -50,7 +62,7 @@ def __init__(self, points_array=None, empirical_semivariance=None, verbose=False
         :param verbose: (bool) if True then all messages are printed, otherwise nothing.
         """
 
-        self.points_values = points_array
+        self.points_array = points_array
         self.empirical_semivariance = empirical_semivariance
         self.verbose = verbose
         self.theoretical_model = None
@@ -204,7 +216,7 @@ def fit_semivariance(self, model_type, number_of_ranges=16):
         self.theoretical_model = model
 
         # sill
-        sill = np.var(self.points_values[:, -1])
+        sill = np.var(self.points_array[:, -1])
 
         # nugget
         if self.empirical_semivariance[0][0] != 0:
@@ -261,7 +273,7 @@ def find_optimal_model(self, weighted=False, number_of_ranges=16):
         base_error = self.calculate_base_error()
 
         # sill
-        sill = np.var(self.points_values[:, -1])
+        sill = np.var(self.points_array[:, -1])
 
         # nugget
         if self.empirical_semivariance[0][0] != 0: