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jni · Nov 20, 2023 · 7c26ec9 · 7c26ec9
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diff --git a/dev/_modules/skan/csr.html b/dev/_modules/skan/csr.html
diff --git a/dev/_modules/skan/draw.html b/dev/_modules/skan/draw.html
@@ -332,7 +332,7 @@ <h1>Source code for skan.draw</h1><div class="highlight"><pre>
         <span class="n">stats</span><span class="p">,</span>
         <span class="o">*</span><span class="p">,</span>
         <span class="n">image_cmap</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">skeleton_color_source</span><span class="o">=</span><span class="s1">&#39;branch-type&#39;</span><span class="p">,</span>
+        <span class="n">skeleton_color_source</span><span class="o">=</span><span class="s1">&#39;branch_type&#39;</span><span class="p">,</span>
         <span class="n">skeleton_colormap</span><span class="o">=</span><span class="s1">&#39;viridis&#39;</span><span class="p">,</span>
         <span class="n">axes</span><span class="o">=</span><span class="kc">None</span>
         <span class="p">):</span>
@@ -356,7 +356,7 @@ <h1>Source code for skan.draw</h1><div class="highlight"><pre>
 
 <span class="sd">        - skeleton-id: each individual skeleton (connected component) will</span>
 <span class="sd">          have a different colour.</span>
-<span class="sd">        - branch-type: each branch type (tip-tip, tip-junction,</span>
+<span class="sd">        - branch_type: each branch type (tip-tip, tip-junction,</span>
 <span class="sd">          junction-junction, path-path). This is the default.</span>
 <span class="sd">        - branch-distance: the curved length of the skeleton branch.</span>
 <span class="sd">        - euclidean-distance: the straight-line length of the skeleton branch.</span>
@@ -374,8 +374,8 @@ <h1>Source code for skan.draw</h1><div class="highlight"><pre>
     <span class="n">image</span> <span class="o">=</span> <span class="n">_normalise_image</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">image_cmap</span><span class="o">=</span><span class="n">image_cmap</span><span class="p">)</span>
     <span class="n">summary</span> <span class="o">=</span> <span class="n">stats</span>
     <span class="c1"># transforming from row, col to x, y</span>
-    <span class="n">coords_cols</span> <span class="o">=</span> <span class="p">([</span><span class="s1">&#39;image-coord-src-</span><span class="si">%i</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</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="o">+</span> <span class="p">[</span><span class="s1">&#39;image-coord-dst-</span><span class="si">%i</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</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">coords_cols</span> <span class="o">=</span> <span class="p">([</span><span class="s1">&#39;image_coord_src_</span><span class="si">%i</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</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="o">+</span> <span class="p">[</span><span class="s1">&#39;image_coord_dst_</span><span class="si">%i</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</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">coords</span> <span class="o">=</span> <span class="n">summary</span><span class="p">[</span><span class="n">coords_cols</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>

diff --git a/dev/_modules/skan/pipe.html b/dev/_modules/skan/pipe.html
@@ -297,13 +297,15 @@ <h1>Source code for skan.pipe</h1><div class="highlight"><pre>
             <span class="p">)</span>
     <span class="n">quality</span> <span class="o">=</span> <span class="n">shape_index</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="n">pixel_smoothing_radius</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;reflect&#39;</span><span class="p">)</span>
     <span class="n">skeleton</span> <span class="o">=</span> <span class="n">morphology</span><span class="o">.</span><span class="n">skeletonize</span><span class="p">(</span><span class="n">thresholded</span><span class="p">)</span> <span class="o">*</span> <span class="n">quality</span>
-    <span class="n">framedata</span> <span class="o">=</span> <span class="n">csr</span><span class="o">.</span><span class="n">summarize</span><span class="p">(</span><span class="n">csr</span><span class="o">.</span><span class="n">Skeleton</span><span class="p">(</span><span class="n">skeleton</span><span class="p">,</span> <span class="n">spacing</span><span class="o">=</span><span class="n">scale</span><span class="p">))</span>
+    <span class="n">framedata</span> <span class="o">=</span> <span class="n">csr</span><span class="o">.</span><span class="n">summarize</span><span class="p">(</span>
+            <span class="n">csr</span><span class="o">.</span><span class="n">Skeleton</span><span class="p">(</span><span class="n">skeleton</span><span class="p">,</span> <span class="n">spacing</span><span class="o">=</span><span class="n">scale</span><span class="p">),</span> <span class="n">separator</span><span class="o">=</span><span class="s1">&#39;_&#39;</span>
+            <span class="p">)</span>
     <span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;squiggle&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span>
-            <span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;branch-distance&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;euclidean-distance&#39;</span><span class="p">]</span>
+            <span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;branch_distance&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;euclidean_distance&#39;</span><span class="p">]</span>
             <span class="p">)</span>
     <span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;scale&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">scale</span>
     <span class="n">framedata</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-            <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;mean-pixel-value&#39;</span><span class="p">:</span> <span class="s1">&#39;mean-shape-index&#39;</span><span class="p">},</span>
+            <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;mean_pixel_value&#39;</span><span class="p">:</span> <span class="s1">&#39;mean_shape_index&#39;</span><span class="p">},</span>
             <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
             <span class="n">errors</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">,</span>
             <span class="p">)</span>
@@ -384,9 +386,9 @@ <h1>Source code for skan.pipe</h1><div class="highlight"><pre>
             <span class="n">image_stats</span><span class="p">[</span><span class="s1">&#39;branch density&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
                     <span class="n">framedata</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">image_stats</span><span class="p">[</span><span class="s1">&#39;area&#39;</span><span class="p">]</span>
                     <span class="p">)</span>
-            <span class="n">j2j</span> <span class="o">=</span> <span class="n">framedata</span><span class="p">[</span><span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;branch-type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">]</span>
+            <span class="n">j2j</span> <span class="o">=</span> <span class="n">framedata</span><span class="p">[</span><span class="n">framedata</span><span class="p">[</span><span class="s1">&#39;branch_type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">]</span>
             <span class="n">image_stats</span><span class="p">[</span><span class="s1">&#39;mean J2J branch distance&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">j2j</span><span class="p">[</span><span class="s1">&#39;branch-distance&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+                    <span class="n">j2j</span><span class="p">[</span><span class="s1">&#39;branch_distance&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
                     <span class="p">)</span>
             <span class="n">image_results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">image_stats</span><span class="p">)</span>
             <span class="k">yield</span> <span class="n">filename</span><span class="p">,</span> <span class="n">image</span><span class="p">,</span> <span class="n">thresholded</span><span class="p">,</span> <span class="n">skeleton</span><span class="p">,</span> <span class="n">framedata</span>

diff --git a/dev/_sources/examples/complete_analysis.md.txt b/dev/_sources/examples/complete_analysis.md.txt
@@ -112,21 +112,21 @@ data['field number'] = data['filename'].apply(field_number)
 Next, we filter the branches by using the [*shape index*](http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.shape_index). We have used a very simple method to extract skeletons (see [Getting started](../getting_started/getting_started)), which does an acceptable job but creates a lot of false branches. Since the goal of Skan is to analyse skeletons, rather than generate them, we attempt to filter the branches, and measure only those that look like ridges according to the shape index.
 
 ```{code-cell} ipython3
-ridges = ((data['mean-shape-index'] < 0.625) &
-          (data['mean-shape-index'] > 0.125))
+ridges = ((data['mean_shape_index'] < 0.625) &
+          (data['mean_shape_index'] > 0.125))
 ```
 
 For the same reason, we only look at junction-to-junction branches, which are more accurately identified by our method than junction-to-endpoint branches.
 
 ```{code-cell} ipython3
-j2j = data['branch-type'] == 2
+j2j = data['branch_type'] == 2
 datar = data.loc[ridges & j2j].copy()
 ```
 
 Finally, we make a new column of measurements in a more natural scale for our purpose.
 
 ```{code-cell} ipython3
-datar['branch distance (nm)'] = datar['branch-distance'] * 1e9
+datar['branch distance (nm)'] = datar['branch_distance'] * 1e9
 ```
 
 ## 3. Making the figure

diff --git a/dev/_sources/examples/visualizing_3d_skeletons.md.txt b/dev/_sources/examples/visualizing_3d_skeletons.md.txt
@@ -49,17 +49,17 @@ all_paths = [
 ```
 
 ```{code-cell} ipython3
-paths_table = skan.summarize(skeleton)
+paths_table = skan.summarize(skeleton, separator='_')
 ```
 
 ```{code-cell} ipython3
-paths_table['path-id'] = np.arange(skeleton.n_paths)
+paths_table['path_id'] = np.arange(skeleton.n_paths)
 ```
 
 First, we color by random path ID, showing each path in a distinct color using the matplotlib "tab10" qualitative palette. (Coloring by path ID directly results in "bands" of nearby paths receiving the same color.)
 
 ```{code-cell} ipython3
-paths_table['random-path-id'] = np.random.default_rng().permutation(skeleton.n_paths)
+paths_table['random_path_id'] = np.random.default_rng().permutation(skeleton.n_paths)
 ```
 
 ```{code-cell} ipython3
@@ -70,7 +70,7 @@ skeleton_layer = viewer.add_shapes(
         shape_type='path',
         properties=paths_table,
         edge_width=0.5,
-        edge_color='random-path-id',
+        edge_color='random_path_id',
         edge_colormap='tab10',
 )
 ```
@@ -85,9 +85,9 @@ napari.utils.nbscreenshot(viewer)
 We can also demonstrate that most of these branches are in one skeleton, with a few stragglers around the edges, by coloring by skeleton ID:
 
 ```{code-cell} ipython3
-skeleton_layer.edge_color = 'skeleton-id'
+skeleton_layer.edge_color = 'skeleton_id'
 # for now, we need to set the face color as well
-skeleton_layer.face_color = 'skeleton-id'
+skeleton_layer.face_color = 'skeleton_id'
 ```
 
 ```{code-cell} ipython3
@@ -99,10 +99,10 @@ napari.utils.nbscreenshot(viewer)
 Finally, we can color the paths by a numerical property, such as their length.
 
 ```{code-cell} ipython3
-skeleton_layer.edge_color = 'branch-distance'
+skeleton_layer.edge_color = 'branch_distance'
 skeleton_layer.edge_colormap = 'viridis'
 # for now, we need to set the face color as well
-skeleton_layer.face_color = 'branch-distance'
+skeleton_layer.face_color = 'branch_distance'
 skeleton_layer.face_colormap = 'viridis'
 ```
 

diff --git a/dev/_sources/getting_started/getting_started.md.txt b/dev/_sources/getting_started/getting_started.md.txt
@@ -136,7 +136,7 @@ Let's go back to the red blood cell image to illustrate this graph.
 
 ```{code-cell} ipython3
 from skan import Skeleton, summarize
-branch_data = summarize(Skeleton(skeleton0, spacing=spacing_nm))
+branch_data = summarize(Skeleton(skeleton0, spacing=spacing_nm), separator='_')
 branch_data.head()
 ```
 
@@ -156,7 +156,7 @@ Next come the coordinates in natural space, the Euclidean distance between the p
 This data table follows the "tidy data" paradigm, with one row per branch, which allows fast  exploration of branch statistics. Here, for example, we plot the distribution of branch lengths according to branch type:
 
 ```{code-cell} ipython3
-branch_data.hist(column='branch-distance', by='branch-type', bins=100);
+branch_data.hist(column='branch_distance', by='branch_type', bins=100);
 ```
 
 We can see that junction-to-junction branches tend to be longer than junction-to-endpoint and junction isolated branches, and that there are no cycles in our dataset.
@@ -165,7 +165,7 @@ We can also represent this visually with the `overlay_euclidean_skeleton`, which
 
 ```{code-cell} ipython3
 draw.overlay_euclidean_skeleton_2d(image0, branch_data,
-                                   skeleton_color_source='branch-type');
+                                   skeleton_color_source='branch_type');
 ```
 
 ## 2. Comparing different skeletons
@@ -194,7 +194,7 @@ def skeletonize(images, spacings_nm):
 
 
 skeletons = skeletonize(images, spacings_nm)
-tables = [summarize(Skeleton(skeleton, spacing=spacing))
+tables = [summarize(Skeleton(skeleton, spacing=spacing), separator='_')
           for skeleton, spacing in zip(skeletons, spacings_nm)]
 
 for filename, dataframe in zip(files, tables):
@@ -210,8 +210,8 @@ Now, however, we have a tidy data table with information about the sample origin
 ```{code-cell} ipython3
 import seaborn as sns
 
-j2j = (table[table['branch-type'] == 2].
-       rename(columns={'branch-distance':
+j2j = (table[table['branch_type'] == 2].
+       rename(columns={'branch_distance':
                        'branch distance (nm)'}))
 per_image = j2j.groupby('filename').median()
 per_image['infected'] = ['infected' if 'inf' in fn else 'normal'