from smartdata import SmartspacesCSVAdapter
adapter = SmartspacesCSVAdapter()

datasets = [
    {'lbl': 'Queens', 'id': 3},
    {'lbl': 'Hugh Aston', 'id': 6},
    {'lbl': 'Kimberlin', 'id': 8},
    {'lbl': 'Campus Centre', 'id': 10},
    {'lbl': 'John Whitehead', 'id': 12}
]
for dataset in datasets:
    dataset['dataframe'] = adapter.dataframe(dataset['id'], 'all')
datasets[0]['dataframe']

fig, axes = subplots(5, 1, figsize=(12, 8))
for i, dataset in enumerate(datasets):
    ax = axes[i]
    ax.plot(dataset['dataframe'].index, dataset['dataframe'], c="black", lw=0.15)
    ax.set_title(dataset['lbl'])
    ax.set_ylabel('consumption\n(kWh)')
tight_layout()

for dataset in datasets:
    dataset['daily_min'] = dataset['dataframe'].resample('D', how='min')
    dataset['daily_sum'] = dataset['dataframe'].resample('D', how='sum')
    dataset['weekly_min'] = dataset['dataframe'].resample('W', how='min')

fmt = mpl.dates.DateFormatter(fmt="%b\n%Y")
loc = mpl.dates.MonthLocator(interval=4)
fig, axes = subplots(5, 1, figsize=(12, 10), sharex=True)
for i, dataset in enumerate(datasets):
    axes[i].set_title(dataset['lbl'])
    axes[i].plot(dataset['dataframe'].index, dataset['dataframe'], label="raw", lw=0.2, alpha=0.5, c='black')
    axes[i].plot(dataset['daily_min'].index, dataset['daily_min'], label="daily", lw=0.2, c='blue')
    axes[i].plot(dataset['weekly_min'].index, dataset['weekly_min'], label="weekly", c='red', lw=1)
    axes[i].xaxis.set_major_locator(loc)
    axes[i].xaxis.set_major_formatter(fmt)
    axes[i].set_ylabel('minimum daily/weekly\nconsumption (kWh)')
    leg = axes[i].legend(loc=2)
tight_layout()

fmt = mpl.dates.DateFormatter(fmt="%b\n%Y")
loc = mpl.dates.MonthLocator(interval=4)
fig, axes = subplots(5, 1, figsize=(12, 10), sharex=True)
for i, dataset in enumerate(datasets):
    baseload_pct = ((dataset['daily_min'].sum()*48 / dataset['daily_sum'].sum()) * 100)
    axes[i].set_title(dataset['lbl'])
#    axes[i].plot(dataset['daily_sum'].index, dataset['daily_sum']['consumption (kWh)'], label='occupied load', color='blue')
#    axes[i].plot(dataset['daily_min'].index, dataset['daily_min']['consumption (kWh)']*48, label='unoccupied load', color='red')
    axes[i].text(0.01, 0.1, "baseload as proportion of total consumption = %3.1f%%" % baseload_pct, transform=axes[i].transAxes)
    axes[i].fill_between(dataset['daily_sum'].index, dataset['daily_min']['consumption (kWh)']*48, dataset['daily_sum']['consumption (kWh)'], label='occupied load', color='blue', alpha=0.5, lw=0.2)
    axes[i].fill_between(dataset['daily_sum'].index, dataset['daily_min']['consumption (kWh)']*48, label='baseload', color='red', alpha=0.5, lw=0.2)
    axes[i].set_ylim(0, axes[i].get_ylim()[1])
    axes[i].xaxis.set_major_locator(loc)
    axes[i].xaxis.set_major_formatter(fmt)
    axes[i].set_ylabel('daily\nconsumption (kWh)')
    leg = axes[i].legend(loc=2)
tight_layout()