134 lines
3.6 KiB
Python
134 lines
3.6 KiB
Python
#!/usr/bin/env python3
|
|
|
|
import sys
|
|
sys.path.insert(1, 'lib')
|
|
import argparse
|
|
import ast
|
|
import csv
|
|
from datetime import datetime
|
|
from geopy.distance import distance
|
|
import io
|
|
import itertools
|
|
import lmdk_lib
|
|
import numpy as np
|
|
import os
|
|
import matplotlib.pyplot as plt
|
|
import time
|
|
import zipfile
|
|
|
|
|
|
# https://cloud.delkappa.com/s/oNWLmM6jrpjK8Ff
|
|
# HUE data format
|
|
# Header size
|
|
hdr = 1
|
|
# Date
|
|
dt = 0
|
|
# Hour
|
|
hr = 1
|
|
# Energy consumption in kWh
|
|
kwh = 2
|
|
# Timestamp format
|
|
tim_fmt = "%Y-%m-%d %H"
|
|
|
|
|
|
def main(args):
|
|
'''
|
|
Load data
|
|
'''
|
|
# Get consumption data [timestamp, consumption] from previous parsing
|
|
cons_data = lmdk_lib.load_data(args, 'cons')
|
|
if cons_data.size == 0:
|
|
# Consumption [dt, hr, kwh]
|
|
cons = []
|
|
try:
|
|
print('Extracting %s... ' %(os.path.abspath(args.arc)), end='', flush=True)
|
|
with zipfile.ZipFile(args.arc, 'r') as arc:
|
|
print('[OK]')
|
|
with io.TextIOWrapper(arc.open(args.dat), newline='\n') as dat:
|
|
try:
|
|
print('Finding consumption... ', end='', flush=True)
|
|
# Get the consumption by skipping the header
|
|
cons_l = list(csv.reader(dat, delimiter=','))[hdr:]
|
|
# Check each line
|
|
for c in cons_l:
|
|
# Add valid consumption
|
|
if c[kwh] != '' and float(c[kwh]) > 0:
|
|
cons.append([datetime.strptime(c[dt] + ' ' + c[hr], tim_fmt).timestamp(), c[kwh]])
|
|
print('[OK]')
|
|
except Exception as e:
|
|
print('[Error: %s]' %(e))
|
|
except Exception as e:
|
|
print('[Error: %s]' %(e))
|
|
# Save the first 1000 rows to the results
|
|
cons_data = np.array(cons, np.float32)[:1000]
|
|
lmdk_lib.save_data(args, cons_data, 'cons')
|
|
'''
|
|
Find thresholds for goals.
|
|
0.2: 1.12
|
|
0.4: 0.88
|
|
0.6: 0.68
|
|
0.8: 0.54
|
|
'''
|
|
# {goal: theta}
|
|
lmdk = {}
|
|
# Percentage of landmarks
|
|
goal = [.2, .4, .6, .8]
|
|
# Find max consumpton to start from
|
|
theta = max(cons_data[:, 1])
|
|
cons_data_cur = np.copy(cons_data)
|
|
# Find thetas for each goal
|
|
for g in goal:
|
|
print('Looking for %.1f... ' %(g), end='', flush=True)
|
|
while theta > 0:
|
|
# Reduce threshold gradually
|
|
theta -= .01
|
|
# Find data below the current theta
|
|
cons_data_cur = cons_data_cur[cons_data_cur[:, 1] < theta]
|
|
# Calculate the percentage of landmarks
|
|
diff = (len(cons_data) - len(cons_data_cur))/len(cons_data)
|
|
# Check if it is close enough to what we need
|
|
if abs(diff - g)/g < .05:
|
|
print('%.2f' %(theta))
|
|
lmdk[g] = theta
|
|
# Continue with the next goal
|
|
break
|
|
|
|
|
|
|
|
'''
|
|
Parse arguments.
|
|
|
|
Optional:
|
|
arc - The data archive file.
|
|
dat - The consumption data file.
|
|
res - The results archive file.
|
|
'''
|
|
def parse_args():
|
|
# Create argument parser.
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# Mandatory arguments.
|
|
|
|
# Optional arguments.
|
|
parser.add_argument('-a', '--arc', help='The data archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Data.zip')
|
|
parser.add_argument('-d', '--dat', help='The consumption data file.', type=str, default='Residential_1.csv')
|
|
parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Results.zip')
|
|
|
|
# Parse arguments.
|
|
args = parser.parse_args()
|
|
|
|
return args
|
|
|
|
|
|
if __name__ == '__main__':
|
|
try:
|
|
start_time = time.time()
|
|
main(parse_args())
|
|
end_time = time.time()
|
|
print('##############################')
|
|
print('Time : %.4fs' % (end_time - start_time))
|
|
print('##############################')
|
|
except KeyboardInterrupt:
|
|
print('Interrupted by user.')
|
|
exit()
|