path_to_data = c.get('data', 'folder')
if not os.path.exists(path_to_data):
raise IOError("Provide your path to the data in your config.ini file. ")
else:
path_to_fig = os.path.join(path_to_data, 'figures')
if not os.path.isdir(path_to_fig): os.makedirs(path_to_fig)
# In[ ]:
c.get('data', 'folder')
# ### Loading meta data and user variables
# In[ ]:
hp = houseprint.Houseprint()
end = pd.Timestamp(time.time(), unit='s')
start = end - pd.Timedelta('21 days')
# In[ ]:
hp.save('new_houseprint.pkl')
# ## Plotting
# In[ ]:
#hp.sync_tmpos()
# ### Water sensors
def compute(sensorid, start_model, end_model):
end = pd.Timestamp('now', tz='Europe/Brussels')
# Create houseprint from saved file, if not available, parse the google spreadsheet
try:
hp_filename = os.path.join(c.get('data', 'folder'), 'hp_anonymous.pkl')
hp = houseprint.load_houseprint_from_file(hp_filename)
print("Houseprint loaded from {}".format(hp_filename))
except Exception as e:
print(e)
print(
"Because of this error we try to build the houseprint from source")
hp = houseprint.Houseprint()
hp.init_tmpo()
# Load the cached daily data
sensor = hp.find_sensor(sensorid)
cache = caching.Cache(variable='{}_daily_total'.format(sensor.type))
df_day = cache.get(sensors=[sensor])
df_day.rename(columns={sensorid: sensor.type}, inplace=True)
# Load the cached weather data, clean up and compose a combined dataframe
weather = forecastwrapper.Weather(location=(50.8024, 4.3407),
start=start_model,
end=end)
irradiances = [
(0, 90), # north vertical
(90, 90), # east vertical
(180, 90), # south vertical
(270, 90), # west vertical
]
orientations = [0, 90, 180, 270]
weather_data = weather.days(
irradiances=irradiances,
wind_orients=orientations,
heating_base_temperatures=[0, 6, 8, 10, 12, 14, 16, 18]).dropna(axis=1)
weather_data.drop([
'icon', 'summary', 'moonPhase', 'windBearing', 'temperatureMaxTime',
'temperatureMinTime', 'apparentTemperatureMaxTime',
'apparentTemperatureMinTime', 'uvIndexTime', 'sunsetTime',
'sunriseTime'
],
axis=1,
inplace=True)
# Add columns for the day-of-week
for i, d in zip(range(7), [
'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
'Sunday'
]):
weather_data[d] = 0
weather_data.loc[weather_data.index.weekday == i, d] = 1
weather_data = weather_data.applymap(float)
data = pd.concat([df_day, weather_data], axis=1).dropna()
data = data.tz_convert('Europe/Brussels')
df = data.resample(rule='MS').sum()
if len(df) < 2:
print("Not enough data for building a monthly reference model")
sys.exit(1)
# monthly model, statistical validation
mv = regression.MVLinReg(df.ix[:end_model], sensor.type, p_max=0.03)
figures = mv.plot(df=df)
figures[0].savefig(os.path.join(c.get('data', 'folder'), 'figures',
'multivar_model_' + sensorid + '.png'),
dpi=100)
figures[1].savefig(os.path.join(c.get('data', 'folder'), 'figures',
'multivar_results_' + sensorid + '.png'),
dpi=100)
# weekly model, statistical validation
df = data.resample(rule='W').sum()
if len(df.ix[:end_model]) < 4:
print("Not enough data for building a weekly reference model")
sys.exit(1)
mv = regression.MVLinReg(df.ix[:end_model], sensor.type, p_max=0.02)
if len(df.ix[end_model:]) > 0:
figures = mv.plot(model=False, bar_chart=True, df=df.ix[end_model:])
figures[0].savefig(os.path.join(
c.get('data', 'folder'), 'figures',
'multivar_prediction_weekly_' + sensorid + '.png'),
dpi=100)