def correlate(chan_id, weather_variable, subplot_index, annotate_y):
# 25 = lighting circuit # (R^2 = 0.443)
# 8 = kitchen lights (R^2 = 0.194)
# 2 = boiler (versus radiation R^2 = 0.052,
# versus mean_temp R^2 = 0.298,
# versus max_temp R^2 = 0.432,
# versus min_temp R^2 = 0.212)
# 3 = solar (R^2 = 0.798)
# 12 = fridge vs min_temp R^2 = 0.255 (with on_power_threshold = 20)
print("Opening channel data...")
channel = Channel(DATA_DIR, chan_id)
print("Calculating...")
channel.on_power_threshold = 20
hours_on = channel.usage_per_period('D', tz_convert='UTC').hours_on
hours_on = hours_on[hours_on > ON_DURATION_THRESHOLD]
hours_on.description = 'hours on'
print("Got {} days of data from usage_per_period.".format(hours_on.size))
print("Plotting...")
x_aligned, y_aligned = pda.stats.align(weather_variable, hours_on)
print(x_aligned.description)
slope, intercept, r_value, p_value, std_err = linregress(x_aligned.values,
y_aligned.values)
ax = fig.add_subplot(2,2,subplot_index)
ax = spfl.format_axes(ax)
ax = pda.stats.plot_regression_line(ax, x_aligned, y_aligned, slope,
intercept, r_value,
annotate_y=annotate_y)
print("R^2={:.3f}".format(r_value**2))
ax.set_title('Correlation between ' + channel.get_long_name() + ' and ' +
metoffice.get_long_name(weather_variable.name))
range=(min_power, max_power),
bins=int(round((max_power-min_power)/2)))
# Draw histogram for unnormalised values
ax.hist(c.series.values,
histtype='step',
color=UNNORMALISED_LINE_COLOR,
alpha=0.5,
range=(min_power, max_power),
bins=int(round((max_power-min_power)/2)))
# format plot
ax.set_axis_bgcolor('#eeeeee')
yticks = ax.get_yticks()
ax.set_yticks([])
spfl.format_axes(ax)
ax.spines['left'].set_visible(False)
if c.name == 'washing_machine':
ax.set_ylim([0, np.max(n)*0.2])
title_x = 0.5
elif c.name in ['bedroom_ds_lamp', 'kitchen_lights']:
ax.set_ylim([0, np.max(n)*1.65])
title_x = 0.5
elif c.name == 'lcd_office':
ax.set_ylim([0, np.max(n)*1.5])
title_x = 0.5
elif c.name == 'htpc':
ax.set_ylim([0, np.max(n)*1.2])
title_x = 0.4
elif c.name == 'breadmaker':
ax.set_ylim([0, np.max(n)*0.5])
range=(min_power, max_power),
bins=int(round((max_power - min_power) / 2)))
# Draw histogram for unnormalised values
ax.hist(c.series.values,
histtype='step',
color=UNNORMALISED_LINE_COLOR,
alpha=0.5,
range=(min_power, max_power),
bins=int(round((max_power - min_power) / 2)))
# format plot
ax.set_axis_bgcolor('#eeeeee')
yticks = ax.get_yticks()
ax.set_yticks([])
spfl.format_axes(ax)
ax.spines['left'].set_visible(False)
if c.name == 'washing_machine':
ax.set_ylim([0, np.max(n) * 0.2])
title_x = 0.5
elif c.name in ['bedroom_ds_lamp', 'kitchen_lights']:
ax.set_ylim([0, np.max(n) * 1.65])
title_x = 0.5
elif c.name == 'lcd_office':
ax.set_ylim([0, np.max(n) * 1.5])
title_x = 0.5
elif c.name == 'htpc':
ax.set_ylim([0, np.max(n) * 1.2])
title_x = 0.4
elif c.name == 'breadmaker':
ax.set_ylim([0, np.max(n) * 0.5])
