<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 This script shows how to use the existing code in opengrid to create a baseload electricity consumption benchmark.
Step1:  Script settings
Step2:  We create one big dataframe, the columns are the sensors of type electricity
Step3:  Convert Datetimeindex to local time
Step5:  We define two low-level functions
Step6:  Data handling
We have to filter out the data, we do three things
Step7:  Plots
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
<ASSISTANT_TASK:>
Python Code:
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 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'))
from opengrid.library.houseprint import houseprint
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
Explanation: This script shows how to use the existing code in opengrid to create a baseload electricity consumption benchmark.
End of explanation
BXL = pytz.timezone('Europe/Brussels')
number_of_days = 7
Explanation: Script settings
End of explanation
hp = houseprint.load_houseprint_from_file('new_houseprint.pkl')
hp.init_tmpo()
start = pd.Timestamp(time.time() - number_of_days*86400, unit='s')
df = hp.get_data(sensortype='electricity', head=start, resample='s')
df = df.resample(rule='60s', how='max')
df = df.diff()*3600/60
Explanation: We create one big dataframe, the columns are the sensors of type electricity
End of explanation
df.index = df.index.tz_convert(BXL)
# plot a few dataframes to inspect them
if DEV:
    for sensor in df.columns:
        plt.figure()
        df[sensor].plot()
Explanation: Convert Datetimeindex to local time
End of explanation
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
Explanation: We define two low-level functions
End of explanation
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()))
  
Explanation: Data handling
We have to filter out the data, we do three things:
split the data in dataframes per day 
filter out the night-time hours (between 00h00 and 05h00)
we check if the resulting time series contain enough variation (negatives and constant signals are filtered out)
End of explanation
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)[:]:
    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.find_sensor(sensor).device.key + ' - ' + 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.find_sensor(sensor).device.key)
    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()
Explanation: Plots
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.
End of explanation
<END_TASK>