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from __future__ import print_function, division
import nilmtk
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import ticker
import numpy as np
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nilmtk.plots.latexify(fig_width=3.33, fontsize=8, fig_height=1.5)
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dataset = nilmtk.DataSet('/data/mine/vadeec/merged/ukdale.h5')
dataset.set_window("2014-01-01", "2014-03-01")
elec = dataset.buildings[1].elec
meter = elec['washing machine']
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activations = meter.get_activations(border=5)
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activation = activations[6]
print("Date =", activation.index[0])
# Create x-axis (minutes)
minutes_per_sample = 0.1
num_minutes = len(activation) * minutes_per_sample
minutes = np.arange(start=0.0, stop=num_minutes, step=minutes_per_sample)[:len(activation)]
# Plot
fig, ax = plt.subplots()
ax.plot(minutes, activation.values / 1000, color='#4878CF', linewidth=0.2, zorder=10)
# Format
ax.xaxis.set_major_locator(ticker.MultipleLocator(30))
ax.set_xlim([0, num_minutes])
ax.yaxis.set_major_locator(ticker.MaxNLocator(3))
ax = nilmtk.plots.format_axes(ax, spine_color='k')
#ax.set_axis_bgcolor((0.95, 0.95, 0.95))
#for spine in ['left', 'bottom']:
# ax.spines[spine].set_visible(False)
#for axis in [ax.xaxis, ax.yaxis]:
# axis.set_tick_params(color=(.8, .8, .8))
#plt.grid(b=True, which='major', color='w', linestyle='-', zorder=0)
ax.set_ylabel('Power (kW)')
ax.set_xlabel('Time (minutes)')
plt.tight_layout()
#plt.subplots_adjust(left=0.0005, bottom=0.05)
plt.savefig(
'/home/dk3810/Dropbox/MyWork/imperial/PhD/writing/papers/BuildSys_2015_Neural_NILM/washer.pdf',
bbox_inches='tight',
pad_inches=0)
#plt.show()
plt.close(fig)
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