Refactor radial_distribution_between_species() (#340)

* Refactor radial_distribution_between_species()

* Add shortcut to radial distribution on trajectory

* Fix test fail

src/gemdat/plots/__init__.py

@@ -21,7 +21,6 @@
     msd_per_element,
     plot_3d,
     radial_distribution,
-    radial_distribution_between_species,
     rectilinear,
     shape,
     vibrational_amplitudes,
@@ -45,7 +44,6 @@
     'msd_per_element',
     'plot_3d',
     'radial_distribution',
-    'radial_distribution_between_species',
     'rectilinear',
     'shape',
     'vibrational_amplitudes',

src/gemdat/plots/_shared.py

@@ -10,8 +10,6 @@
 from scipy.stats import skewnorm
 
 if TYPE_CHECKING:
-    from typing import Collection
-
     from gemdat.orientations import Orientations
     from gemdat.trajectory import Trajectory
 
@@ -149,45 +147,3 @@ def _get_vibrational_amplitudes_hist(
     std = np.std(data, axis=0)
 
     return VibrationalAmplitudeHist(amplitudes=amplitudes, counts=mean, std=std)
-
-
-def _get_radial_distribution_between_species(
-    *,
-    trajectory: Trajectory,
-    specie_1: str | Collection[str],
-    specie_2: str | Collection[str],
-    max_dist: float = 5.0,
-    resolution: float = 0.1,
-) -> tuple[np.ndarray, np.ndarray]:
-    coords_1 = trajectory.filter(specie_1).coords
-    coords_2 = trajectory.filter(specie_2).coords
-    lattice = trajectory.get_lattice()
-
-    if coords_2.ndim == 2:
-        num_time_steps = 1
-        num_atoms, num_dimensions = coords_2.shape
-    else:
-        num_time_steps, num_atoms, num_dimensions = coords_2.shape
-
-    particle_vol = num_atoms / lattice.volume
-
-    all_dists = np.concatenate(
-        [
-            lattice.get_all_distances(coords_1[t, :, :], coords_2[t, :, :])
-            for t in range(num_time_steps)
-        ]
-    )
-    distances = all_dists.flatten()
-
-    bins = np.arange(0, max_dist + resolution, resolution)
-    rdf, _ = np.histogram(distances, bins=bins, density=False)
-
-    def normalize(radius: np.ndarray) -> np.ndarray:
-        """Normalize bin to volume."""
-        shell = (radius + resolution) ** 3 - radius**3
-        return particle_vol * (4 / 3) * np.pi * shell
-
-    norm = normalize(bins)[:-1]
-    rdf = rdf / norm
-
-    return bins, rdf

src/gemdat/plots/matplotlib/__init__.py

@@ -17,7 +17,6 @@
 from ._jumps_vs_time import jumps_vs_time
 from ._msd_per_element import msd_per_element
 from ._radial_distribution import radial_distribution
-from ._radial_distribution_between_species import radial_distribution_between_species
 from ._rectilinear import rectilinear
 from ._shape import shape
 from ._vibrational_amplitudes import vibrational_amplitudes
@@ -37,7 +36,6 @@
     'jumps_vs_time',
     'msd_per_element',
     'radial_distribution',
-    'radial_distribution_between_species',
     'rectilinear',
     'shape',
     'vibrational_amplitudes',

src/gemdat/plots/matplotlib/_radial_distribution.py

@@ -26,13 +26,14 @@ def radial_distribution(rdfs: Iterable[RDFData]) -> matplotlib.figure.Figure:
     fig, ax = plt.subplots()
 
     for rdf in rdfs:
-        ax.plot(rdf.x, rdf.y, label=rdf.symbol)
+        ax.plot(rdf.x, rdf.y, label=rdf.label)
 
-    states = ', '.join({rdf.state for rdf in rdfs})
+    states = ', '.join({rdf.state for rdf in rdfs if rdf.state})
+    state_suffix = f' ({states})' if states else ''
 
     ax.legend()
     ax.set(
-        title=f'Radial distribution function ({states})',
+        title=f'Radial distribution function{state_suffix}',
         xlabel='Distance (Å)',
         ylabel='Counts',
     )

src/gemdat/plots/plotly/__init__.py

@@ -17,7 +17,6 @@
 from ._msd_per_element import msd_per_element
 from ._plot3d import plot_3d
 from ._radial_distribution import radial_distribution
-from ._radial_distribution_between_species import radial_distribution_between_species
 from ._rectilinear import rectilinear
 from ._shape import shape
 from ._vibrational_amplitudes import vibrational_amplitudes
@@ -38,7 +37,6 @@
     'msd_per_element',
     'plot_3d',
     'radial_distribution',
-    'radial_distribution_between_species',
     'rectilinear',
     'shape',
     'vibrational_amplitudes',

src/gemdat/plots/plotly/_radial_distribution.py

@@ -28,15 +28,17 @@ def radial_distribution(rdfs: Iterable[RDFData]) -> go.Figure:
             go.Scatter(
                 x=rdf.x,
                 y=rdf.y,
-                name=rdf.symbol,
+                name=rdf.label,
                 mode='lines',
                 # line={'width': 0.25}
             )
         )
 
-    states = ', '.join({rdf.state for rdf in rdfs})
+    states = ', '.join({rdf.state for rdf in rdfs if rdf.state})
+    state_suffix = f' ({states})' if states else ''
+
     fig.update_layout(
-        title=f'Radial distribution function ({states})',
+        title=f'Radial distribution function{state_suffix}',
         xaxis_title='Distance (Å)',
         yaxis_title='Counts',
     )

src/gemdat/rdf.py

@@ -10,8 +10,11 @@
 from ._plot_backend import plot_backend
 
 if TYPE_CHECKING:
+    from typing import Collection
+
     from pymatgen.core import Structure
 
+    from gemdat import Trajectory
     from gemdat.transitions import Transitions
 
 
@@ -79,16 +82,16 @@ class RDFData:
         1D array with x data (bins)
     y : np.ndarray
         1D array with y data (counts)
-    symbol : str
-        Distance to species with this symbol
+    label : str
+        Distance to species with this symbol label
     state : str
         State that the floating species is in, e.g.
         the jump that it is making.
     """
 
     x: np.ndarray
     y: np.ndarray
-    symbol: str
+    label: str
     state: str
 
     @plot_backend
@@ -178,10 +181,75 @@ def radial_distribution(
             x=bins,
             # Drop last element with distance > max_dist
             y=values[:-1],
-            symbol=symbol,
+            label=symbol,
             state=state,
         )
         ret.setdefault(state, RDFCollection())
         ret[state].append(rdf_data)
 
     return ret
+
+
+def radial_distribution_between_species(
+    *,
+    trajectory: Trajectory,
+    specie_1: str | Collection[str],
+    specie_2: str | Collection[str],
+    max_dist: float = 5.0,
+    resolution: float = 0.1,
+) -> RDFData:
+    """Calculate RDFs from specie_1 to specie_2.
+
+    Parameters
+    ----------
+    trajectory: Trajectory
+        Input trajectory.
+    specie_1: str | list[str]
+        Name of specie or list of species
+    specie_2: str | list[str]
+        Name of specie or list of species
+    max_dist: float, optional
+        Max distance for rdf calculation
+    resolution: float, optional
+        Width of the bins
+
+    Returns
+    -------
+    rdf : RDFData
+        RDF data for the given species.
+    """
+    coords_1 = trajectory.filter(specie_1).coords
+    coords_2 = trajectory.filter(specie_2).coords
+    lattice = trajectory.get_lattice()
+
+    if coords_2.ndim == 2:
+        num_time_steps = 1
+        num_atoms, num_dimensions = coords_2.shape
+    else:
+        num_time_steps, num_atoms, num_dimensions = coords_2.shape
+
+    particle_vol = num_atoms / lattice.volume
+
+    all_dists = np.concatenate(
+        [
+            lattice.get_all_distances(coords_1[t, :, :], coords_2[t, :, :])
+            for t in range(num_time_steps)
+        ]
+    )
+    distances = all_dists.flatten()
+
+    bins = np.arange(0, max_dist + resolution, resolution)
+    rdf, _ = np.histogram(distances, bins=bins, density=False)
+
+    def normalize(radius: np.ndarray) -> np.ndarray:
+        """Normalize bin to volume."""
+        shell = (radius + resolution) ** 3 - radius**3
+        return particle_vol * (4 / 3) * np.pi * shell
+
+    norm = normalize(bins)[:-1]
+    counts = rdf / norm
+
+    str1 = specie_1 if isinstance(specie_1, str) else '/'.join(specie_1)
+    str2 = specie_1 if isinstance(specie_2, str) else '/'.join(specie_2)
+
+    return RDFData(x=bins[:-1], y=counts, label=f'{str1}-{str2}', state='')