This script shows how to use the existing code in opengrid to create a water consumption benchmark.



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import os
import sys
import inspect
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tmpo

from opengrid import config
c = config.Config()

%matplotlib inline
plt.rcParams['figure.figsize'] = 16,8



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try:
    if os.path.exists(c.get('tmpo', 'data')):
        path_to_tmpo_data = c.get('tmpo', 'data')
except:
    path_to_tmpo_data = None



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from opengrid.library.houseprint import houseprint

We create one dataframe containing a column for each water sensor



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#hp = houseprint.Houseprint()



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#hp.save('new_houseprint.pkl')



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hp = houseprint.load_houseprint_from_file('new_houseprint.pkl')
hp.init_tmpo()



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#hp.sync_tmpos()



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df = hp.get_data(sensortype='water')



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#1st derivative
df = df.diff()

#retain only positive values (sometimes there are meter resets)
df = df[df > 0]



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# plot timeseries and load duration for each retained sensor

for sensor in df.columns:
    plt.figure()
    ts = df[sensor].dropna()
    ax1=plt.subplot(121)
    plt.plot_date(ts.index, ts, '-', label=sensor)
    plt.ylabel('water consumption [l/min]')
    plt.legend()
    
    ax2=plt.subplot(122)
    plt.plot(np.sort(ts)[::-1], label=sensor)
    plt.ylabel('water JBDC [l/min]')
    plt.legend()



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for sensor in df.columns:
    plt.figure()
    ts = df[sensor]
    ts=ts[ts>1]
    ts=ts[ts<30]
    ts.hist(bins=200)
    plt.title(hp.find_sensor(sensor).device.key + ' - ' + sensor)



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for sensor in df.columns:
        
    plt.figure()
    ts = df[sensor]
    ts=ts[ts>0.1]
    ts=ts[ts<30]
    
    hst, edges = np.histogram(ts, np.arange(0,20,0.25))
    hst_weighted = hst * edges[1:]
    plt.plot(np.arange(0,20,0.25)[1:], hst_weighted)
    plt.plot(np.arange(0,20,0.25)[1:], hst)
    plt.title(hp.find_sensor(sensor).device.key + ' - ' + sensor)



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delattr(hp, '_tmpos')



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df.tail()



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