docs: add optional dependency detail for hvplot and seaborn

docs/source/user_guide/installation.rst

@@ -59,6 +59,16 @@ The steps to install PyProBE are as follows:
 
       pip install PyProBE-Data
 
+   Optional dependencies can be added to the installation as follows:
+
+   .. code-block:: bash
+
+      pip install 'PyProBE-Data[hvplot]'
+
+   If a method uses an optional dependency, it will be detailed in the 
+   :doc:`api documentation <pyprobe>`. If these methods is run without their dependencies
+   installed, they will return an error. 
+
 3. You can create a new python script or jupyter notebook to 
    process your data. You can import PyProBE into your script as follows:
 

pyprobe/plot.py

@@ -70,7 +70,8 @@ def __getattr__(self, _: Any) -> None:
                 """Raise an ImportError if seaborn is not installed."""
                 raise ImportError(
                     "Optional dependency 'seaborn' is not installed. Please install by "
-                    "running 'pip install seaborn'."
+                    "running 'pip install seaborn' or installing PyProBE with seaborn "
+                    "as an optional dependency: `pip install 'PyProBE-Data[seaborn]'."
                 )
 
         return SeabornWrapper()
@@ -124,11 +125,17 @@ def wrapper(*args: Any, **kwargs: Any) -> Any:
 seaborn = _create_seaborn_wrapper()
 """A wrapped version of the seaborn package.
 
+Requires the seaborn package to be installed as an optional dependency. You can install
+it with PyProBE by running :code:`pip install 'PyProBE-Data[seaborn]'`, or install it
+seperately with :code:`pip install seaborn`.
+
 This version of seaborn is modified to work with PyProBE Result objects. All functions
 from the original seaborn package are available in this version. Where seaborn functions
 accept a 'data' argument, a PyProBE Result object can be passed instead of a pandas
 DataFrame. For example:
 
+.. code-block:: python
+
     from pyprobe.plot import seaborn as sns
 
     result = cell.procedure['Sample']

pyprobe/result.py

@@ -237,21 +237,27 @@ def hvplot(self, *args: Any, **kwargs: Any) -> None:
             data_to_plot = _retrieve_relevant_columns(self, args, kwargs)
             return data_to_plot.hvplot(*args, **kwargs)
 
-        hvplot.__doc__ = (
-            "HvPlot is a library for creating fast and interactive plots.\n\n"
-            "The default backend is bokeh, which can be changed by setting the backend "
-            "with :code:`hvplot.extension('matplotlib')` or "
-            ":code:`hvplot.extension('plotly')`.\n\n" + (hvplot.__doc__ or "")
-        )
     else:
 
         def hvplot(self, *args: Any, **kwargs: Any) -> None:
             """Wrapper for plotting using the hvplot library."""
             raise ImportError(
                 "Optional dependency hvplot is not installed. Please install it via "
-                "'pip install hvplot'."
+                "'pip install hvplot' or by installing PyProBE with hvplot as an "
+                "optional dependency: pip install 'PyProBE-Data[hvplot]'."
             )
 
+    hvplot.__doc__ = (
+        "HvPlot is a library for creating fast and interactive plots.\n\n"
+        "This method requires the hvplot library to be installed as an optional "
+        "dependency. You can install it with PyProBE by running "
+        ":code:`pip install 'PyProBE-Data[hvplot]'`, or install it seperately with "
+        ":code:`pip install hvplot`.\n\n"
+        "The default backend is bokeh, which can be changed by setting the backend "
+        "with :code:`hvplot.extension('matplotlib')` or "
+        ":code:`hvplot.extension('plotly')`.\n\n" + (hvplot.__doc__ or "")
+    )
+
     def _get_data_subset(self, *column_names: str) -> pl.DataFrame:
         """Return a subset of the data with the specified columns.