def compare_activities(activities, room, conf):
#sensor_tint = ["lrl1"]
sensor_power = ["brac"]
sensor_text = ["wuconditions"]
#df_tint = compare_sensors(activities, room, conf, "temperature_ds18b20", sensor_tint)
df_power = data_frame(activities, room, conf, ["power"], sensor_power)
df_text = data_frame(activities, room, conf, ["temperature_ds18b20"], sensor_text)
return df_power, df_text
def peak_analysis(activities, room, conf):
sensors = ["power", "temperature_ds18b20"]
locations = dict()
locations["temperature_ds18b20"] = ["wuconditions"]
if room[0] == "Bedroom":
locations["power"] = ["brac"]
else:
locations["power"] = ["lr"]
df = data_frame(activities, room, conf, sensors, locations)
peaks = numpy.array([])
fig = plt.figure(figsize=(18,12), facecolor='w', edgecolor='k')
fig.suptitle(room[0] + " Peak Power Analysis", fontsize=14, fontweight='bold')
idx = 1
power_cols = filter(lambda x: 'Power' in x, df.columns)
plots = 100*(numpy.ceil(len(power_cols)/2)+1) + 20
results = pandas.DataFrame(index=activities)
for column in power_cols:
power_df = df[column]
if len(power_df[power_df < 1500]) == 0:
clusters = 1
else:
clusters = 3
peak_value, labels = generate_clusters(power_df, clusters = clusters)
peaks = numpy.append(peaks, peak_value)
activity = column.split('_')[1]
val = activity + '_Temperature'
text_col = filter(lambda x: val in x, df.columns)
results.loc[activity, "Peak"] = peak_value
results.loc[activity, "Text_Avg"] = df[text_col[0]].mean()
ax = plt.subplot(plots + idx)
scatter_plot(power_df, labels, ax, activity)
idx = idx + 1
ax1 = plt.subplot(plots + idx) # Create matplotlib axes
results.to_csv('Results/Stats/Raw/' + room[0] + '_Summary.csv')
yy_plot(results["Peak"], results["Text_Avg"], ax1)
fig.tight_layout()
plt.savefig('Results/Figures/Raw/' + room[0] + '_Plot.png')
return results, df
