Merge tag '0.4.2' into develop

0.4.2

Demo.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Current version: 0.3.3"
+    "### Current version: 0.4.2"
    ]
   },
   {
@@ -36,7 +36,7 @@
    "metadata": {},
    "source": [
     "### Or you can cd into the site-packages subfolder of your python distribution (or environment) and clone from there:\n",
-    "##### `$ git clone http://github.com/milcent/Benford_py.git`"
+    "##### `$ git clone http://github.com/milcent/benford_py.git`"
    ]
   },
   {

README-pypi.md

@@ -20,7 +20,7 @@ If you find *Benford_py* useful in your research, please consider adding the fol
 
 --------------------------------------------------------------------------------
 
-`current version = 0.4.1`
+`current version = 0.4.2`
 
 ### See [release notes](https://github.com/milcent/benford_py/releases/) for features in this and in older versions
 

README.md

@@ -20,7 +20,7 @@ If you find *Benford_py* useful in your research, please consider adding the fol
 
 --------------------------------------------------------------------------------
 
-`current version = 0.4.1`
+`current version = 0.4.2`
 
 ### See [release notes](https://github.com/milcent/benford_py/releases/) for features in this and in older versions
 

__init__.py

@@ -1,2 +1,2 @@
 '''Benfords law module'''
-__version__ = "0.4.1"
+__version__ = "0.4.2"

benford/__init__.py

@@ -24,4 +24,4 @@
 
 from .benford import *
 
-__version__ = '0.4.1'
+__version__ = '0.4.2'

docs/build/html/_modules/benford/stats.html

@@ -151,8 +151,8 @@
            <div itemprop="articleBody">
 
   <h1>Source code for benford.stats</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">sqrt</span><span class="p">,</span> <span class="n">log</span><span class="p">,</span> <span class="n">where</span>
-<span class="kn">from</span> <span class="nn">.constants</span> <span class="kn">import</span> <span class="n">crit_chi2</span><span class="p">,</span> <span class="n">KS_crit</span><span class="p">,</span> <span class="n">mad_dict</span><span class="p">,</span> <span class="n">digs_dict</span>
+<span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">errstate</span><span class="p">,</span> <span class="n">log</span><span class="p">,</span> <span class="n">sqrt</span><span class="p">,</span> <span class="n">where</span>
+<span class="kn">from</span> <span class="nn">.constants</span> <span class="kn">import</span> <span class="n">CRIT_CHI2</span><span class="p">,</span> <span class="n">CRIT_KS</span><span class="p">,</span> <span class="n">MAD_CONFORM</span><span class="p">,</span> <span class="n">DIGS</span>
 
 
 <div class="viewcode-block" id="Z_score"><a class="viewcode-back" href="../../api.html#benford.stats.Z_score">[docs]</a><span class="k">def</span> <span class="nf">Z_score</span><span class="p">(</span><span class="n">frame</span><span class="p">,</span> <span class="n">N</span><span class="p">):</span>
@@ -194,7 +194,7 @@ <h1>Source code for benford.stats</h1><div class="highlight"><pre>
         <span class="n">exp_counts</span> <span class="o">=</span> <span class="n">frame</span><span class="o">.</span><span class="n">Counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">*</span> <span class="n">frame</span><span class="o">.</span><span class="n">Expected</span>
         <span class="n">dif_counts</span> <span class="o">=</span> <span class="n">frame</span><span class="o">.</span><span class="n">Counts</span> <span class="o">-</span> <span class="n">exp_counts</span>
         <span class="n">found_chi</span> <span class="o">=</span> <span class="p">(</span><span class="n">dif_counts</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="n">exp_counts</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">crit_chi</span> <span class="o">=</span> <span class="n">crit_chi2</span><span class="p">[</span><span class="n">ddf</span><span class="p">][</span><span class="n">confidence</span><span class="p">]</span>
+        <span class="n">crit_chi</span> <span class="o">=</span> <span class="n">CRIT_CHI2</span><span class="p">[</span><span class="n">ddf</span><span class="p">][</span><span class="n">confidence</span><span class="p">]</span>
         <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">The Chi-square statistic is </span><span class="si">{</span><span class="n">found_chi</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">.</span><span class="se">\n</span><span class="s2">&quot;</span>
                   <span class="sa">f</span><span class="s2">&quot;Critical Chi-square for this series: </span><span class="si">{</span><span class="n">crit_chi</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
@@ -241,7 +241,7 @@ <h1>Source code for benford.stats</h1><div class="highlight"><pre>
         <span class="c1"># finding the supremum - the largest cumul dist difference</span>
         <span class="n">suprem</span> <span class="o">=</span> <span class="p">((</span><span class="n">ks_frame</span><span class="o">.</span><span class="n">Found</span> <span class="o">-</span> <span class="n">ks_frame</span><span class="o">.</span><span class="n">Expected</span><span class="p">)</span><span class="o">.</span><span class="n">abs</span><span class="p">())</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
         <span class="c1"># calculating the crittical value according to confidence</span>
-        <span class="n">crit_KS</span> <span class="o">=</span> <span class="n">KS_crit</span><span class="p">[</span><span class="n">confidence</span><span class="p">]</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
+        <span class="n">crit_KS</span> <span class="o">=</span> <span class="n">CRIT_KS</span><span class="p">[</span><span class="n">confidence</span><span class="p">]</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">N</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">The Kolmogorov-Smirnov statistic is </span><span class="si">{</span><span class="n">suprem</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">.</span><span class="se">\n</span><span class="s2">&quot;</span>
@@ -285,11 +285,11 @@ <h1>Source code for benford.stats</h1><div class="highlight"><pre>
         <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">The Mean Absolute Deviation is </span><span class="si">{</span><span class="n">mad</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">test</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">2</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;For the </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">digs_dict</span><span class="p">[</span><span class="n">test</span><span class="p">]]</span><span class="si">}</span><span class="s2">:</span><span class="se">\n\</span>
-<span class="s2">            - 0.0000 to </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">: Close Conformity</span><span class="se">\n\</span>
-<span class="s2">            - </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">: Acceptable Conformity</span><span class="se">\n\</span>
-<span class="s2">            - </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s2">: Marginally Acceptable Conformity</span><span class="se">\n\</span>
-<span class="s2">            - Above </span><span class="si">{</span><span class="n">mad_dict</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s2">: Nonconformity&quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;For the </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">DIGS</span><span class="p">[</span><span class="n">test</span><span class="p">]]</span><span class="si">}</span><span class="s2">:</span><span class="se">\n\</span>
+<span class="s2">            - 0.0000 to </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">: Close Conformity</span><span class="se">\n\</span>
+<span class="s2">            - </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">: Acceptable Conformity</span><span class="se">\n\</span>
+<span class="s2">            - </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s2">: Marginally Acceptable Conformity</span><span class="se">\n\</span>
+<span class="s2">            - Above </span><span class="si">{</span><span class="n">MAD_CONFORM</span><span class="p">[</span><span class="n">test</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s2">: Nonconformity&quot;</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">pass</span>
     <span class="k">return</span> <span class="n">mad</span></div>
@@ -358,7 +358,10 @@ <h1>Source code for benford.stats</h1><div class="highlight"><pre>
 <span class="sd">    Returns:</span>
 <span class="sd">        kulb_leib_diverg (float)        </span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">log</span><span class="p">((</span><span class="n">dist_1</span> <span class="o">/</span> <span class="n">dist_2</span><span class="p">),</span> <span class="n">where</span><span class="o">=</span><span class="p">(</span><span class="n">dist_1</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">))</span> <span class="o">*</span> <span class="n">dist_1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="c1"># ignore divide by zero warning in np.where</span>
+    <span class="k">with</span> <span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s1">&#39;ignore&#39;</span><span class="p">):</span>
+        <span class="n">kl_d</span> <span class="o">=</span> <span class="p">(</span><span class="n">log</span><span class="p">((</span><span class="n">dist_1</span> <span class="o">/</span> <span class="n">dist_2</span><span class="p">),</span> <span class="n">where</span><span class="o">=</span><span class="p">(</span><span class="n">dist_1</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">))</span> <span class="o">*</span> <span class="n">dist_1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">kl_d</span>
 </pre></div>
 
            </div>