import os, sys
import inspect
import numpy as np
import datetime as dt
import time
import pytz
import pandas as pd
import pdb
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# add the path to opengrid to sys.path
sys.path.append(os.path.join(script_dir, os.pardir, os.pardir))
from opengrid.library import houseprint
from opengrid.library import fluksoapi
from opengrid.library import config
c=config.Config()
DEV = c.get('env', 'type') == 'dev' # DEV is True if we are in development environment, False if on the droplet
if not DEV:
# production environment: don't try to display plots
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.dates import HourLocator, DateFormatter, AutoDateLocator
# find tmpo
sys.path.append(c.get('tmpo', 'folder'))
import tmpo
if DEV:
if c.get('env', 'plots') == 'inline':
%matplotlib inline
else:
%matplotlib qt
else:
pass # don't try to render plots
plt.rcParams['figure.figsize'] = 12,8
BXL = pytz.timezone('Europe/Brussels')
number_of_days = 7
hp = houseprint.load_houseprint_from_file('hp_anonymous.pkl')
tmpos = tmpo.Session()
sensors = hp.get_sensors_by_type('electricity')
#sensors.remove('b325dbc1a0d62c99a50609e919b9ea06')
series = []
start = pd.Timestamp(time.time() - number_of_days*86400, unit='s')
for sensor in sensors:
s = tmpos.series(sid=sensor, head=start)
series.append(s.resample(rule='60s', how='max'))
dfi = pd.concat(series, axis=1)
dfi = dfi.interpolate(method='time')
df = dfi.diff()*3600/60
df.index = df.index.tz_convert(BXL)
# plot a few dataframes to inspect them
if DEV:
for sensor in sensors[:3]:
if sensor in df.columns:
plt.figure()
df[sensor].plot()
We define two low-level functions
def testvalid(row):
return row['maxima'] > 0 and row['maxima'] <> row['minima']
def get_minima(sensor):
"""
Return the standby consumption for the covered days for a given sensor as an array.
Take care of days where this sensor has NO VALID standby consumption
"""
global minima
res = np.ndarray(len(minima))
for i,df in enumerate(minima):
try:
res[i] = df[sensor]
except:
res[i] = np.nan
return res
We have to filter out the data, we do three things:
index_slices = [] # will contain the correct index slices for each of the analysed nights
minima = [] # each element in minima is a dataframe with standby consumption per valid sensor
valid_sensors = set() # we keep track of all sensors that yield a valid standby consumption for at least one day.
# find the date for which we still have the full night (between 01:00 and 05:00). We will store it as datetime at 00:00 (local time)
hour = df.index[-1].hour # the hour of the last index.
if hour >= 5:
last_day = df.index[-1].date()
else:
last_day = (df.index[-1] - dt.timedelta(days=1)).date()
for day in range(number_of_days)[::-1]:
#pdb.set_trace()
dt_start = dt.datetime.combine(last_day - dt.timedelta(days=day), dt.time(0,0)) # start slicing at 01:00 local time
dt_stop = dt.datetime.combine(last_day - dt.timedelta(days=day), dt.time(5,0)) # stop slicing at 05:00 local time
df_night = df.ix[dt_start:dt_stop] # contains only data for a single night
index_slices.append(df_night.index.copy())
df_results = pd.DataFrame(index=df.columns) #df_results contains the results of the analysis for a single night. Index = sensorid
df_results['minima'] = df_night.min(axis=0)
df_results['maxima'] = df_night.max(axis=0)
df_results['valid'] = df_results.apply(testvalid, axis=1)
minima.append(df_results['minima'].ix[df_results.valid])
valid_sensors.update(set(minima[-1].index.tolist()))
The next plots are the current benchmarks, anonymous. The left figure shows where the given sensor (or family) is situated compared to all other families. The right plot shows the night-time consumption for this night.
In a next step, it would be nice to create an interactive plot (D3.js?) for the right side: it should show the night-time consumption for the day over which the mouse hovers in the left graph.
index_slices_days = [x[0] for x in index_slices[1:]]
index = pd.DatetimeIndex(freq='D', start=index_slices_days[0], periods=number_of_days)
df_=pd.concat(minima, axis=1)
df_.columns = index
df_statistics = df_.describe().T
df_statistics
for sensor in list(valid_sensors)[:2]:
plt.figure(figsize=(10,8))
ax1=plt.subplot(211)
ax1.plot_date(df_statistics.index, df_statistics[u'25%'], '-', lw=2, color='g', label=u'25%')
ax1.plot_date(df_statistics.index, df_statistics[u'50%'], '-', lw=2, color='orange', label=u'50%')
ax1.plot_date(df_statistics.index, df_statistics[u'75%'], '-', lw=2, color='r', label=u'75%')
ax1.plot_date(df_.T.index, df_.T[sensor], 'rD', ms=7)
xticks = [x.strftime(format='%d/%m') for x in df_statistics.index]
locs, lables=plt.xticks()
plt.xticks(locs, xticks, rotation='vertical')
plt.title(hp.get_flukso_from_sensor(sensor) + ' - ' + sensor)
ax1.grid()
ax1.set_ylabel('Watt')
ax2=plt.subplot(212)
try:
ax2.plot(index_slices[-1], df.ix[index_slices[-1]][sensor], 'b-', label='Afgelopen nacht')
ax2.xaxis_date(BXL) #Put timeseries plot in local time
# rotate the labels
plt.xticks(rotation='vertical')
plt.legend()
ax2.set_ylabel('Watt')
except:
print "Could not create graph for {}".format(hp.get_flukso_from_sensor(sensor))
else:
plt.savefig(os.path.join(c.get('data', 'folder'), 'figures', 'standby_vertical_'+sensor+'.png'), dpi=100)
if not DEV:
plt.close()
try:
valid_sensors.remove('565de0a7dc64d8370aa321491217b85f') # the FLM of 3E does not fit in household standby benchmark
except:
pass
for sensor in valid_sensors:
plt.figure(figsize=(10,5))
ax1=plt.subplot(121)
box = [x.values for x in minima]
ax1.boxplot(box, positions=range(len(df_statistics)), notch=False)
ax1.plot(range(len(df_statistics)), get_minima(sensor), 'rD', ms=10, label='Sluipverbruik')
xticks = [x[0].strftime(format='%d/%m') for x in index_slices]
plt.xticks(range(len(df_statistics)), xticks, rotation='vertical')
#plt.title(hp.get_flukso_from_sensor(sensor) + ' - ' + sensor)
ax1.grid()
ax1.set_ylabel('Watt')
plt.legend(numpoints=1, frameon=False)
#ax1.set_xticklabels([t.strftime(format='%d/%m') for t in df_all_perday.index.tolist()])
ax2=plt.subplot(122)
try:
ax2.plot(index_slices[-1], df.ix[index_slices[-1]][sensor], 'b-', label='Afgelopen nacht')
ax2.xaxis_date(BXL) #Put timeseries plot in local time
# rotate the labels
plt.xticks(rotation='vertical')
ax2.set_ylabel('Watt')
ax2.grid()
plt.legend(loc='upper right', frameon=False)
plt.tight_layout()
except Exception as e:
print(e)
else:
plt.savefig(os.path.join(c.get('data', 'folder'), 'figures', 'standby_horizontal_'+sensor+'.png'), dpi=100)
if not DEV:
plt.close()