Merge pull request #391 from jo-mueller/use-sorted-semi-axes-for-curv…

…ature-calculation

fix axes ordering for curvature calculation

.github/workflows/test_and_deploy.yml

@@ -19,7 +19,7 @@ jobs:
     runs-on: ${{ matrix.platform }}
     strategy:
       matrix:
-        platform: [windows-latest, ubuntu-latest, macos-latest] # ubuntu-latest is failing anyway
+        platform: [windows-latest, ubuntu-latest] # ubuntu-latest is failing anyway
         python-version: ['3.9', '3.10', '3.11']
 
     steps:

docs/02_interactive_usage/02_example_workflows/visualize_measurements_in_viewer.md

@@ -1,9 +1,7 @@
 (point_and_click:visualize_features)=
 # Visualize measurements
 
-Napari-stress (and the [measurement toolbox](toolboxes:stress_toolbox:stress_toolbox_interactive), in partciular) generate a number of measurements that can be visualized using the [napari-matplotlib](https://napari-matplotlib.github.io/) plugin. It is automatically installed with napari-stress and can be activated from the plugins menu (`Plugins > napari Matplotlib`).
-
-In order to use it, check out the documentation on the [napari-matplotlib page](https://napari-matplotlib.github.io/). In the scope of napari-stress, what you will need, is the Features Histogram (`Plugins > napari Matplotlib > FeaturesHistogram`).
+Napari-stress (and the [measurement toolbox](toolboxes:stress_toolbox:stress_toolbox_interactive), in partciular) generate a number of measurements that can be visualized using the [napari-matplotlib](https://napari-matplotlib.github.io/) plugin. In order to use it, check out the documentation on the [napari-matplotlib page](https://napari-matplotlib.github.io/). In the scope of napari-stress, what you will need, is the Features Histogram (`Plugins > napari Matplotlib > FeaturesHistogram`).
 
 ![](./imgs/demo_visualize_featureHistogram.png)
 

setup.cfg

@@ -36,22 +36,20 @@ install_requires =
     joblib
     mpmath
     napari
-    napari-matplotlib>=1.1.0
-    napari-process-points-and-surfaces>=0.4.0
+    napari-process-points-and-surfaces
     napari-segment-blobs-and-things-with-membranes
     napari-tools-menu>=0.1.15
     numpy<1.24.0
     pandas
     pygeodesic
-    pyocclient
     scikit-image
     scipy>=1.9.0
     seaborn
     tqdm
     vedo>=2023.5.0
     vispy
     deprecation
-    bokeh >= 3.1.0
+    #bokeh >= 3.1.0
 
 python_requires = >=3.7
 include_package_data = True

src/napari_stress/_approximation/expansion.py

@@ -554,11 +554,11 @@ def properties(self):
             The maximum and minimum curvatures :math:`H_{max}` and :math:`H_{min}` are calculated as follows:
 
             .. math::
-                H_{max} = 1 / (2 * a^2) + 1 / (2 * b^2)
+                H_{max} = a / (2 * c^2) + a / (2 * b^2)
 
-                H_{min} = 1 / (2 * b^2) + 1 / (2 * a^2)
+                H_{min} = c / (2 * b^2) + c / (2 * a^2)
 
-            where a, b are the largest and smallest axes of the ellipsoid, respectively.
+            where a, b and c are the lengths of the ellipsoid axes along the three spatial dimensions.
         """
         return self._properties
 
@@ -572,20 +572,14 @@ def _measure_max_min_curvatures(self):
 
         """
         # get and remove the largest, smallest and medial axis
-        axes = list(self._axes)
-        largest_axis = max(axes)
-        axes.remove(largest_axis)
-        smallest_axis = min(axes)
-        axes.remove(smallest_axis)
-        medial_axis = axes[0]
+        semi_axis_sorted = np.sort(self._axes)
+        a = semi_axis_sorted[2]
+        b = semi_axis_sorted[1]
+        c = semi_axis_sorted[0]
 
         # accoording to paper (https://www.biorxiv.org/content/10.1101/2021.03.26.437148v1.full)
-        maximum_mean_curvature = largest_axis / (
-            2 * smallest_axis**2
-        ) + largest_axis / (2 * medial_axis**2)
-        minimum_mean_curvature = smallest_axis / (
-            2 * medial_axis**2
-        ) + smallest_axis / (2 * largest_axis**2)
+        maximum_mean_curvature = a / (2 * c**2) + a / (2 * b**2)
+        minimum_mean_curvature = c / (2 * b**2) + c / (2 * a**2)
 
         self._properties["maximum_mean_curvature"] = maximum_mean_curvature
         self._properties["minimum_mean_curvature"] = minimum_mean_curvature
@@ -672,7 +666,7 @@ def _extract_characteristics(self, coefficients: np.ndarray):
                     coefficients[6] / 2.0,
                     coefficients[7] / 2.0,
                     coefficients[8] / 2.0,
-                    -1,
+                    coefficients[9],
                 ],
             ]
         )

src/napari_stress/_measurements/toolbox.py

@@ -500,8 +500,12 @@ def comprehensive_analysis(
     )
 
     max_min_anisotropy = (
-        Expander_ellipsoid.properties["maximum_mean_curvature"]
-        - Expander_ellipsoid.properties["minimum_mean_curvature"]
+        2
+        * gamma
+        * (
+            Expander_ellipsoid.properties["maximum_mean_curvature"]
+            - Expander_ellipsoid.properties["minimum_mean_curvature"]
+        )
     )
 
     result = measurements.tissue_stress_tensor(

src/napari_stress/_measurements/toolbox.ui

@@ -69,7 +69,7 @@
          <number>3</number>
         </property>
         <property name="value">
-         <double>3.000000000000000</double>
+         <double>3.300000000000000</double>
         </property>
        </widget>
       </item>