#-------------------------------------------
fig = plt.figure()
n_subplots = len(chans)
for c, c_normalised in zip(chans, normalised):
subplot_index = chans.index(c) + 1
# hard-coded tweaks for individual appliances
if c.name == 'kitchen_lights':
# Ignore 50W halogens; only use data after all lights replaced by LEDs
c_normalised = c_normalised.crop(datetime.datetime(year=2013, month=4, day=27))
c_normalised.series = c_normalised.series[c_normalised.series > 3]
c_normalised.series = c_normalised.series[c_normalised.series < 5000]
c = c.crop(c_normalised.series.index[0], c_normalised.series.index[-1])
c.series = c.series[c.series > 3]
c.series = c.series[c.series < 5000]
# First get unconstrained histogram from which we will
# automatically find a sensible range
hist, bin_edges = np.histogram(c.series, bins=100)
above_threshold = np.where(hist > MINIMUM_BIN_COUNT)[0]
if len(above_threshold) < 1:
print(c.name, c.chan, " does not have enough data above threshold")
print(bin_edges)
print(hist)
continue
min_power = int(round(bin_edges[above_threshold[0]]))
fig = plt.figure()
n_subplots = len(chans)
for c, c_normalised in zip(chans, normalised):
subplot_index = chans.index(c) + 1
# hard-coded tweaks for individual appliances
if c.name == 'kitchen_lights':
# Ignore 50W halogens; only use data after all lights replaced by LEDs
c_normalised = c_normalised.crop(
datetime.datetime(year=2013, month=4, day=27))
c_normalised.series = c_normalised.series[c_normalised.series > 3]
c_normalised.series = c_normalised.series[c_normalised.series < 5000]
c = c.crop(c_normalised.series.index[0], c_normalised.series.index[-1])
c.series = c.series[c.series > 3]
c.series = c.series[c.series < 5000]
# First get unconstrained histogram from which we will
# automatically find a sensible range
hist, bin_edges = np.histogram(c.series, bins=100)
above_threshold = np.where(hist > MINIMUM_BIN_COUNT)[0]
if len(above_threshold) < 1:
print(c.name, c.chan, " does not have enough data above threshold")
print(bin_edges)
print(hist)
continue
min_power = int(round(bin_edges[above_threshold[0]]))
CHAN_IDS = [14,22]
spfl.setup()
GRID = False
TITLE_Y = 0.75
XTICKS_ON = True
LATEX_PDF_OUTPUT_FILENAME = os.path.join(FIGURE_PATH,
'weekly_usage_histograms'+FIGURE_SUFFIX)
else:
CHAN_IDS = []
CHANS = []
for chan_id in CHAN_IDS:
# Get channel data
print("Loading channel", chan_id)
c = Channel(DATA_DIR, chan_id)
c = c.crop(START_DATE, END_DATE)
CHANS.append(c)
if FIGURE_PRESET == 'boiler seasons':
BIN_SIZE = 'T' # D (daily) or H (hourly) or T (minutely)
TIMESPAN = 'D' # D (daily) or W (weekly)
spfl.setup()
GRID = False
TITLE_Y = 0.7
XTICKS_ON = True
LATEX_PDF_OUTPUT_FILENAME = os.path.join(FIGURE_PATH,
'seasonal_variation'+FIGURE_SUFFIX)
print("Loading winter boiler data...")
winter_boiler = Channel(DATA_DIR, 2)
winter_boiler = winter_boiler.crop(datetime.datetime(year=2013, month=2, day=1),
datetime.datetime(year=2013, month=3, day=1))
CHAN_IDS = [14, 22]
spfl.setup()
GRID = False
TITLE_Y = 0.75
XTICKS_ON = True
LATEX_PDF_OUTPUT_FILENAME = os.path.join(
FIGURE_PATH, 'weekly_usage_histograms' + FIGURE_SUFFIX)
else:
CHAN_IDS = []
CHANS = []
for chan_id in CHAN_IDS:
# Get channel data
print("Loading channel", chan_id)
c = Channel(DATA_DIR, chan_id)
c = c.crop(START_DATE, END_DATE)
CHANS.append(c)
if FIGURE_PRESET == 'boiler seasons':
BIN_SIZE = 'T' # D (daily) or H (hourly) or T (minutely)
TIMESPAN = 'D' # D (daily) or W (weekly)
spfl.setup()
GRID = False
TITLE_Y = 0.7
XTICKS_ON = True
LATEX_PDF_OUTPUT_FILENAME = os.path.join(
FIGURE_PATH, 'seasonal_variation' + FIGURE_SUFFIX)
print("Loading winter boiler data...")
winter_boiler = Channel(DATA_DIR, 2)
winter_boiler = winter_boiler.crop(
datetime.datetime(year=2013, month=2, day=1),
