Add analysis period filtering to utci_comfort_band_comparison plot

Add the ability to filter each HourlyContinuousCollection with an AnalysisPeriod to the utci_comfort_band_comparison plot.

Here is a rough version of something that works. It needs additional verification, e.g. to ensure that the length of the analysis_periods is the same as the length of the utci_collections, it is handled if this is not the case, and comments need updating. Add any additional necessary imports too. Further comments welcome.

```python
def utci_comfort_band_comparison(
utci_collections: tuple[HourlyContinuousCollection],
ax: plt.Axes = None,
identifiers: tuple[str] = None,
utci_categories: CategoricalComfort = UTCI_DEFAULT_CATEGORIES,
density: bool = True,
analysis_periods = [],
**kwargs,
) -> plt.Axes:
"""Create a proportional bar chart showing how different UTCI collections
compare in terms of time within each comfort band.

Args:
utci_collections (list[HourlyContinuousCollection]):
A list of UTCI collections.
ax (plt.Axes, optional):
The matplotlib Axes to plot on. Defaults to None which uses the current Axes.
identifiers (list[str], optional):
A list of names to give each collection. Defaults to None.
utci_categories (Categories, optional):
The UTCI categories to use. Defaults to UTCI_DEFAULT_CATEGORIES.
density (bool, optional):
If True, then show percentage, otherwise show count. Defaults to True.
**kwargs:
Additional keyword arguments to pass to the function.

Returns:
plt.Axes:
A matplotlib Axes object.
"""

for n, col in enumerate(utci_collections):
if not isinstance(col.header.data_type, LB_UniversalThermalClimateIndex):
raise ValueError(
f"Collection {n} data type is not UTCI and cannot be used in this plot."
)
if any(len(i) != len(utci_collections[0]) for i in utci_collections):
raise ValueError("All collections must be the same length.")

if ax is None:
ax = plt.gca()

# set the title
ax.set_title(kwargs.pop("title", None))

if identifiers is None:
identifiers = [f"{n}" for n in range(len(utci_collections))]
if len(identifiers) != len(utci_collections):
raise ValueError(
"The number of identifiers given does not match the number of UTCI collections given!"
)

#new stuff starts here
if len(analysis_periods) > 0 and len(analysis_periods) == len(utci_collections):
series_zips = zip(utci_collections, analysis_periods)
serieses = [collection_to_series(utci_collection.filter_by_analysis_period(analysis_period)) for utci_collection, analysis_period in series_zips]

counts = pd.concat(
[utci_categories.value_counts(i, density=density) for i in serieses],
axis=1,
keys=identifiers,
)
counts.T.plot(
ax=ax,
kind="bar",
stacked=True,
color=utci_categories.colors,
width=0.8,
legend=False,
)

# rest of file.....
```