Add option to add categories to plotting API

[jwnorm]

I did something like this already in a Jupyter Notebook, just need to decide the best way to integrate it into the package. Ideally, I would like it to be something as simple as by_category=True in the existing functions/methods (like in the other OO functions), but the code may be too verbose and might require a separate, make_stacked_hills function or something.

[jwnorm]

plt.style.use('seaborn-darkgrid')
fig1 = plt.figure(figsize=(15, 10))
ax1 = fig1.add_subplot(1, 1, 1)

# infer number of days being plotted
rows = len(summary_df) / len(sorted_dict.keys())
num_days = rows / (60 / bin_size_minutes * 24)

# Create a list to use as the X-axis values
num_bins = num_days * 1440 / bin_size_minutes
base_date_for_first_dow = '01/05/2015'  # Pick any date with associated DOW you want to appear first on plot
timestamps = pd.date_range(base_date_for_first_dow, periods=num_bins, freq=f'{bin_size_minutes}Min').tolist()

# Choose appropriate major and minor tick locations
major_tick_locations = pd.date_range(f'{base_date_for_first_dow} 12:00:00', periods=7, freq='24H').tolist()
minor_tick_locations = pd.date_range(f'{base_date_for_first_dow} 06:00:00', periods=42, freq='4H').tolist()

# Set the tick locations for the axes object
ax1.set_xticks(major_tick_locations)
ax1.set_xticks(minor_tick_locations, minor=True)

# manually add in pct line
pct = df_dict['summaries']['nonstationary']['dow_binofday']['occupancy']['p95'].to_numpy()

# Styling of bars, lines, plot area
# Style the bars for mean occupancy
bar_color = 'steelblue'

# Add data to the plot
# Mean occupancy as bars - here's the GOTCHA involving the bar width
bar_width = 1 / (1440 / bin_size_minutes)

cumulative_array = np.zeros(int(rows))
for i in sorted_dict.keys():
    ax1.bar(timestamps, sorted_dict[i], label=i, width=bar_width, bottom=cumulative_array)
    cumulative_array += sorted_dict[i]

ax1.plot(timestamps, pct, linestyle=pctile_line_style, label=f'95th %ile Occupancy', color=pctile_color)

[jwnorm]

Alternatively, wondering if it would be easier to understand if a category-based plot was broken into small multiples. Sometimes it is difficult to track the size of the stacked bars since they don't line up perfectly.

[misken]

I've wanted a stacked bar plot by category for a while now too. It's probably complex enough that it will end up needing its own function but, maybe not.

I also like idea of a faceted version. They get at different things. Stacks better for how the parts contribute to the whole, but facets better for the details of each part.