Tidying up repr methods

docs/source/api/demography_api.md

@@ -27,18 +27,26 @@ kernelspec:
     :members:
 ```
 
-## The {mod}`~pyrealm.demography.community` module
+## The {mod}`~pyrealm.demography.t_model_functions` module
 
 ```{eval-rst}
-.. automodule:: pyrealm.demography.community
+.. automodule:: pyrealm.demography.t_model_functions
     :autosummary:
     :members:
 ```
 
-## The {mod}`~pyrealm.demography.t_model_functions` module
+## The {mod}`~pyrealm.demography.crown` module
 
 ```{eval-rst}
-.. automodule:: pyrealm.demography.t_model_functions
+.. automodule:: pyrealm.demography.crown
+    :autosummary:
+    :members:
+```
+
+## The {mod}`~pyrealm.demography.community` module
+
+```{eval-rst}
+.. automodule:: pyrealm.demography.community
     :autosummary:
     :members:
 ```

docs/source/users/demography/crown.md

@@ -94,7 +94,7 @@ ax2.set_aspect("equal")
 
 The examples below show the calculation of crown profiles for a set of plant functional
 types (PFTs) with differing crown trait values. We first need to create a
-:class:`~pyrealm.demography.flora.Flora` object that defines those PFTs.
+{class}`~pyrealm.demography.flora.Flora` object that defines those PFTs.
 
 ```{code-cell}
 flora = Flora(
@@ -223,6 +223,9 @@ for pft_idx, offset, colour in zip((0, 1, 2), (0, 5, 12), ("r", "g", "b")):
         linestyle=":",
     )
 
+    ax.set_xlabel("Crown profile")
+    ax.set_ylabel("Height above ground (m)")
+
 ax.set_aspect(aspect=1)
 ```
 
@@ -275,8 +278,6 @@ for pft_idx, offset, colour in zip((0, 1, 2), (0, 5, 10), ("r", "g", "b")):
         color=colour,
         linestyle="--",
     )
-```
-
-```{code-cell}
-
+    ax.set_xlabel("Projected area (m2)")
+    ax.set_ylabel("Height above ground (m)")
 ```

pyrealm/demography/crown.py

@@ -391,6 +391,12 @@ class CrownProfile:
     projected_leaf_area: NDArray[np.float32] = field(init=False)
     """An array of the projected leaf area of stems at z heights"""
 
+    # Information attributes
+    _n_pred: int = field(init=False)
+    """The number of predictions per stem."""
+    _n_stems: int = field(init=False)
+    """The number of stems."""
+
     def __post_init__(
         self,
         stem_traits: StemTraits | Flora,
@@ -431,3 +437,18 @@ def __post_init__(
             stem_height=stem_allometry.stem_height,
             z_max=stem_allometry.crown_z_max,
         )
+
+        # Set the number of observations per stem (one if dbh is 1D, otherwise size of
+        # the first axis)
+        if self.relative_crown_radius.ndim == 1:
+            self._n_pred = 1
+        else:
+            self._n_pred = self.relative_crown_radius.shape[0]
+
+        self._n_stems = stem_traits._n_stems
+
+    def __repr__(self) -> str:
+        return (
+            f"CrownProfile: Prediction for {self._n_stems} stems "
+            f"at {self._n_pred} observations."
+        )

pyrealm/demography/t_model_functions.py

@@ -715,6 +715,7 @@ class StemAllometry:
     """Crown radius scaling factor (-)"""
     crown_z_max: NDArray[np.float32] = field(init=False)
     """Height of maximum crown radius (metres)"""
+
     # Information attributes
     _n_pred: int = field(init=False)
     """The number of predictions per stem."""
@@ -789,7 +790,7 @@ def __post_init__(
 
     def __repr__(self) -> str:
         return (
-            f"StemAllometry: Prediction for {self._n_stems} stems at se"
+            f"StemAllometry: Prediction for {self._n_stems} stems "
             f"at {self._n_pred} DBH values."
         )
 
@@ -860,6 +861,12 @@ class StemAllocation:
     delta_foliage_mass: NDArray[np.float32] = field(init=False)
     """Predicted increase in foliar mass from growth allocation (g C)"""
 
+    # Information attributes
+    _n_pred: int = field(init=False)
+    """The number of predictions per stem."""
+    _n_stems: int = field(init=False)
+    """The number of stems."""
+
     def __post_init__(
         self,
         stem_traits: Flora | StemTraits,
@@ -936,6 +943,6 @@ def __post_init__(
 
     def __repr__(self) -> str:
         return (
-            f"StemAllocation: Prediction for {self._n_stems} stems"
+            f"StemAllocation: Prediction for {self._n_stems} stems "
             f"at {self._n_pred} observations."
         )