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datareader.py
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datareader.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Oct 26 10:45:12 2020
@author: giamm
"""
from pathlib import Path
import numpy as np
import csv
import math
##############################################################################
# This scripted is used to create methods that properly read files
##############################################################################
# The base path is saved in the variable basepath, it is used to move among
# directories to find the files that need to be read.
basepath = Path(__file__).parent
##############################################################################
# if True:
# dirname = 'Parameters'
# filename = 'parameters'
# delimit = ';'
def read_param(filename,delimit,dirname):
''' The function reads from a .csv file in which some parameters are saved.
The file has the parameter's name in the first column, its value
in the second one and its unit of measure (uom) in the third one.
Inputs:
filname - string containing the name of the file (extension of the file: .dat)
delimit - string containing the delimiting element
dirname - name of the folder where to find the file to be opened and read
Outputs:
params - dict, containing the parameters (keys) and their values, as entered by
by the user and stored in the .csv file
'''
dirname = dirname.strip()
filename = filename.strip()
if not filename.endswith('.csv'): filename = filename + '.csv'
fpath = basepath / dirname
params = {}
try:
with open(fpath / filename, mode='r') as csv_file:
csv_reader = csv.reader(csv_file,delimiter=delimit,quotechar="'")
header_row = 1
for row in csv_reader:
if header_row == 1:
for ii in range(len(row)): row[ii] = row[ii].lower().strip().replace(' ', '_')
header = {
'name': row.index('name'),
'value': row.index('value'),
}
header_row = 0
continue
else:
param_name = row[header['name']]
param_val = row[header['value']]
try:
param_val = int(param_val)
except:
try: param_val = float(param_val)
except: param_val = param_val
params[param_name] = param_val
except:
print('Unable to open this file')
# print('Im returning params: {}'.format(params))
return(params)
##############################################################################
def read_general(filename,delimit,dirname):
''' The function reads from a .csv file in which the header is a single row
Inputs:
filname - string containing the name of the file (extension of the file: .dat)
delimit - string containing the delimiting element
dirname - name of the folder where to find the file to be opened and read
Outputs:
data - 2d-array containing the values in the file'
'''
dirname = dirname.strip()
filename = filename.strip()
if not filename.endswith('.csv'): filename = filename + '.csv'
fpath = basepath / dirname
data_list=[]
try:
with open(fpath / filename, mode='r') as csv_file:
csv_reader = csv.reader(csv_file,delimiter=delimit)
next(csv_reader, None)
for row in csv_reader:
data_list.append(row)
except:
print('')
# print('Unable to open this file')
# Creating a 2D-array containing the data(time in the first column and power in the second one)
data = np.array(data_list,dtype='float')
return(data)
##############################################################################
def read_appliances(filename, delimit, dirname):
''' The function reads from a .csv file that contains all the appliances
Inputs:
filname - string containing the name of the file (extension of the file: .dat)
delimit - string containing the delimiting element
dirname - name of the folder where to find the file to be opened and read
Outputs:
app - 2D-array containing, for each appliance, its attributes' values
app_ID - dictionary containing for ID (key) the related appliance's name'
app_attributes - dictionary containing for each attribute (columns in app) its description and unit of measure
'''
dirname = dirname.strip()
filename = filename.strip()
if not filename.endswith('.csv'): filename = filename + '.csv'
fpath = basepath / dirname
# Initializing a list that contains, for each appliance, the numerical values of its attributes
apps_list = []
# Initializing a dictionary that contains, for each appliance, its ID number, nickname, type, weekly and seasonal behaviour and class
apps_ID = {}
# Reading the CSV file
with open(fpath / filename, mode='r') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=delimit)
# Initializing a flag (header_row) that is used to properly treat the header
header_row = 1
for row in csv_reader:
if header_row == 1:
header = row
# Skipping the second row, that contains the units of measure of the attributes
next(csv_reader)
header_row = 0
continue
else:
# Storing only the numeircal values in app_list
apps_list.append(row[7:])
# Storing the non-numerical values (ID, nickname and so on) in app_ID
apps_ID[row[1].lower().replace(' ', ';')] = (int(row[0]), row[2], row[3], row[4].split(','), row[5].split(','), row[6])
# Creating a dictionary that contains the appliances' attributes
apps_attributes = {}
ii = 0
for attr in header:
if attr.lower().replace(' ',';') == 'name': continue
apps_attributes[attr.lower().replace(' ', '_')] = ii
ii += 1
# Creating a 2D-array containing appliances and attributes
apps = np.array(apps_list, dtype = 'float')
return(apps, apps_ID, apps_attributes)
##############################################################################
def read_enclasses(filename, delimit, dirname):
''' The function reads from a .csv file that contains, for each appliance, its yearly energy consumption (kWh/year) for every energetic class
Inputs:
filname - string containing the name of the file (extension of the file: .dat)
delimit - string containing the delimiting element
dirname - name of the folder where to find the file to be opened and read
Outputs:
enclass_en - 2D-array containing, for each appliance (rows), and for each energetic class (columns) the yearly energy consumption (kWh/year)
enclass_levels - dictionary containing for each energetic class (columns in app) its level
'''
apps_ID = read_appliances('eltdome_report', ';', 'Input')[1]
dirname = dirname.strip()
filename = filename.strip()
if not filename.endswith('.csv'): filename = filename + '.csv'
fpath = basepath / dirname
# Initializing a dictionary that contains, for each appliance, its nominal yearly energy consumption for all energy classes
enclass_dict = {}
# Reading the CSV file
with open(fpath / filename, mode = 'r') as csv_file:
csv_reader = csv.reader(csv_file, delimiter = delimit)
# Initializing a flag (header_row) that is used to properly treat the header
header_row = 1
for row in csv_reader:
if header_row == 1:
header = row
# Skipping the second row, that contains the units of measure of the attributes
next(csv_reader)
header_row = 0
continue
else:
# Storing the yearly energy consumption for each energy class (values), for each appliance (keys)
enclass_dict[row[0].lower().replace(' ', ';')] = row[1:]
# Creating a dictionary for energetic classes' levels
enclass_levels = {}
ii = 0
for attr in header[1:]:
enclass_levels[attr] = ii
ii += 1
# Number of energetic classes (needed in the next step)
enclass_n = len(enclass_levels)
# A list is initialized, where the yearly energy consumptions for each appliance can be stored,
# after being sorted is the same order as apps_ID (values are initialized to -1, so that exceptions will
# occur later on, if an appliance is present in apps_ID but not in the enclass_dict)
enclass_sorted = [[-1]*enclass_n]*len(apps_ID)
for app in enclass_dict:
enclass_sorted[apps_ID[app][0]] = enclass_dict[app]
# Creating a 2D-array containing appliances and nominal energy consumptions
enclass_en = np.array(enclass_sorted, dtype='float')
return(enclass_en, enclass_levels)
##############################################################################
def read_energy(filename, delimit, dirname):
''' The function reads from a .csv file that contains, for each appliance, its yearly energy consumption (kWh/year) for every household.
Inputs:
filname - string containing the name of the file (extension of the file: .dat)
delimit - string containing the delimiting element
dirname - name of the folder where to find the file to be opened and read
Outputs:
energy - 2d-array containing in each cell the value of the seasonal energy consumption from each appliance (rows) for each household (columns)
'''
dirname = dirname.strip()
filename = filename.strip()
if not filename.endswith('.csv'): filename = filename + '.csv'
fpath = basepath / dirname
energy_list = [] #list containing, for each appliance,its nominal energy consumption
# Reading the CSV file
with open(fpath / filename, mode = 'r') as csv_file:
csv_reader = csv.reader(csv_file, delimiter = delimit)
line_count = 0
for row in csv_reader:
if line_count == 0:
line_count += 1
continue
else:
energy_list.append(row[2:])
line_count += 1
# Creating a 2D-array containing appliances and nominal energy consumptions
energy = np.array(energy_list,dtype='float')
return(energy)
##############################################################################
# apps, apps_ID, apps_attributes = read_appliances('eltdome_report', ';', 'Input')
# print(apps)
# print(apps_ID)
# print(apps_attributes)
# en_class, en_class_levels = read_enclasses('classenerg_report', ';', 'Input')
# print(en_class)
# print(en_class_levels)
# coeff_matrix, seasons_dict = read_enclasses('coeff_matrix', ';', 'Input')
# print(coeff_matrix)
# print(seasons_dict)