Fixes #143, return_surface and return_midpoint

AUTHORS.rst

@@ -10,3 +10,4 @@ Authors (chronological)
 * Paul Smith
 * Raquel Lopez-Rios
 * David Goh
+* Rubén Chaves

CONTRIBUTING.rst

@@ -42,35 +42,40 @@ To set up `lipyphilic` for local development:
     conda create -n lipyphilic-dev -c conda-forge python=3.10 pip
     conda activate lipyphilic-dev
 
-2. Fork `lipyphilic <https://github.com/p-j-smith/lipyphilic>`_
+2. Install and activate Rust::
+
+    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+    source $HOME/.cargo/env
+
+3. Fork `lipyphilic <https://github.com/p-j-smith/lipyphilic>`_
    (look for the "Fork" button).
 
-3. Clone your fork locally::
+4. Clone your fork locally::
 
     git clone [email protected]:YOURGITHUBNAME/lipyphilic.git
 
-4. Install an editible version of `lipyphilic` along with its development dependencies:
+5. Install an editible version of `lipyphilic` along with its development dependencies:
 
     cd lipyphilic
     python -m pip install -e ".[dev]"
 
-4. Create a branch for local development::
+6. Create a branch for local development::
 
     git checkout -b name-of-your-bugfix-or-feature
 
    Now you can make your changes locally.
 
-5. When you're done making changes run all the checks and docs builder with `tox <https://tox.readthedocs.io/en/latest/install.html>`_ one command::
+7. When you're done making changes run all the checks and docs builder with `tox <https://tox.readthedocs.io/en/latest/install.html>`_ one command::
 
     tox
 
-6. Commit your changes and push your branch to GitHub::
+8. Commit your changes and push your branch to GitHub::
 
     git add .
     git commit -m "Your detailed description of your changes."
     git push origin name-of-your-bugfix-or-feature
 
-7. Submit a pull request through the GitHub website.
+9. Submit a pull request through the GitHub website.
 
 Pull Request Guidelines
 -----------------------

src/lipyphilic/analysis/memb_thickness.py

@@ -169,7 +169,8 @@ def __init__(self, universe, lipid_sel, leaflets, n_bins=1, interpolate=False, r
             of higher-resolution grids. The default is False.
         return_surface : bool, optional
             If True, the height of the bilayer at grid point at each frame is returned as
-            numpy ndarray. The default is False.
+            numpy ndarray. The 2D grid will be stored in self.memb_thickness_grid. The
+            default is False.
 
         Tip
         ---
@@ -276,15 +277,15 @@ def _single_frame(self):
             lower_surface = self._interpolate(lower_surface)
 
         thickness = (
-            np.mean(upper_surface - lower_surface)
+            np.nanmean(upper_surface - lower_surface)
             if self.n_bins > 1
             else (upper_surface - lower_surface)[0, 0]
         )
 
         self.results.memb_thickness[self._frame_index] = thickness
 
         if self._return_surface:
-            self.memb_thickness_grid[self._frame_index] = upper_surface - lower_surface
+            self.memb_thickness_grid[self._frame_index] = upper_surface - lower_surface if self.n_bins > 1 else thickness
 
     def _interpolate(self, surface):
         """Interpolate the leaflet intrinsic surface.

src/lipyphilic/analysis/z_positions.py

@@ -136,7 +136,7 @@
 class ZPositions(AnalysisBase):
     """Calculate the :math:`z` position of molecules in a bilayer."""
 
-    def __init__(self, universe, lipid_sel, height_sel, n_bins=1):
+    def __init__(self, universe, lipid_sel, height_sel, n_bins=1, return_midpoint=False):
         """Set up parameters for calculating :math:`z` positions.
 
         Parameters
@@ -155,6 +155,9 @@ def __init__(self, universe, lipid_sel, height_sel, n_bins=1):
             positions calculated based on the distance to their local membrane midpoint. The
             default is `1`, which is equivalent to computing a single global
             midpoint.
+        return_midpoint : bool, optional
+            If True, the height of the midpoint at grid point at each frame is returned as
+            numpy ndarray. The 2D grid will be stored in self.memb_midpoint. The default is False.
 
         Note
         ----
@@ -185,6 +188,7 @@ def __init__(self, universe, lipid_sel, height_sel, n_bins=1):
 
         self.n_bins = n_bins
         self.results.z_positions = None
+        self._return_midpoint = return_midpoint
 
     @property
     def z_positions(self):
@@ -197,6 +201,11 @@ def _prepare(self):
             fill_value=np.NaN,
         )
 
+        if self._return_midpoint:
+            self.memb_midpoint = np.full(
+                (self.n_frames, self.n_bins, self.n_bins),
+                fill_value=np.NaN)
+
     def _single_frame(self):
         # Atoms must be wrapped before creating a lateral grid of the membrane
         self.membrane.wrap(inplace=True)
@@ -221,6 +230,8 @@ def _single_frame(self):
             bins=(x_bins, y_bins),
             expand_binnumbers=True,
         )
+        if self._return_midpoint:
+            self.memb_midpoint[self._frame_index] = memb_midpoint_xy.statistic if self.n_bins > 1 else memb_midpoint_xy.statistic[0, 0]
 
         # The height in z of each lipid is calculated as the mean heigh
         # of its selected atoms