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Cleaning up the code
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eggimasv authored and eggimasv committed Feb 28, 2017
1 parent 795d681 commit b8b6c6c
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Showing 6 changed files with 143 additions and 238 deletions.
22 changes: 11 additions & 11 deletions energy_demand/building_stock_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ def get_hlc(dw_type, age):
hlc = linear_fits_hlc[dw_type][0] * age + linear_fits_hlc[dw_type][1]
return hlc

class BuildingStockRegion(object):
class DwStockRegion(object):
"""Class of the building stock in a region"""
# TODO: Include old and new stock

Expand Down Expand Up @@ -122,7 +122,7 @@ def get_sum_scenario_driver_lighting(self):
sum_driver += dwelling.scenario_driver_lighting
return sum_driver

def get_floor_area_pp(reg_floor_area, reg_pop, global_variables, assump_final_diff_floor_area_pp):
def get_floor_area_pp(reg_floor_area, reg_pop, glob_var, assump_final_diff_floor_area_pp):
""" Calculates future floor area per person depending on
assumptions on final change and base year data
Expand All @@ -134,7 +134,7 @@ def get_floor_area_pp(reg_floor_area, reg_pop, global_variables, assump_final_di
reg_pop : dict
Population of base year for all region
global_variables : dict
glob_var : dict
Contains all global simulation variables
assump_final_diff_floor_area_pp : float
Expand All @@ -151,8 +151,8 @@ def get_floor_area_pp(reg_floor_area, reg_pop, global_variables, assump_final_di

# initialisation
data_floor_area_pp = {}
sim_period = range(global_variables['base_year'], global_variables['end_year'] + 1, 1) #base year, current year, iteration step
base_year = global_variables['base_year']
sim_period = range(glob_var['base_year'], glob_var['end_year'] + 1, 1) #base year, current year, iteration step
base_year = glob_var['base_year']

# Iterate regions
for reg_id in reg_pop:
Expand All @@ -161,7 +161,7 @@ def get_floor_area_pp(reg_floor_area, reg_pop, global_variables, assump_final_di

# Iterate simulation years
for y in sim_period:
curr_year = y - global_variables['base_year']
curr_year = y - glob_var['base_year']

if y == base_year:
sim_years[y] = floor_area_pp_by # base year value
Expand All @@ -174,7 +174,7 @@ def get_floor_area_pp(reg_floor_area, reg_pop, global_variables, assump_final_di

return data_floor_area_pp

def get_dwtype_dist(base_dwtype_distr, assump_dwtype_distr, global_variables):
def get_dwtype_dist(base_dwtype_distr, assump_dwtype_distr, glob_var):
"""Calculates the yearly distribution of dw types
based on assumption of distribution on end_year
Expand All @@ -190,7 +190,7 @@ def get_dwtype_dist(base_dwtype_distr, assump_dwtype_distr, global_variables):
assump_dwtype_distr : dict
Distribution of dwelling types end year
global_variables : dict
glob_var : dict
Contains all global simulation variables
Returns
Expand All @@ -200,11 +200,11 @@ def get_dwtype_dist(base_dwtype_distr, assump_dwtype_distr, global_variables):
"""

dwtype_distr = {}
sim_period = range(global_variables['base_year'], global_variables['end_year'] + 1, 1) #base year, current year, iteration step
sim_period = range(glob_var['base_year'], glob_var['end_year'] + 1, 1) #base year, current year, iteration step

# Iterate years
for current_year in sim_period:
sim_year = current_year - global_variables['base_year']
sim_year = current_year - glob_var['base_year']
y_distr = {}

# iterate type
Expand Down Expand Up @@ -235,7 +235,7 @@ def get_dwtype_age_distr(get_dwtype_age_distr_base):
assump_dwtype_distr : dict
Distribution of dwelling types end year
global_variables : dict
glob_var : dict
Contains all global simulation variables
Returns
Expand Down
182 changes: 45 additions & 137 deletions energy_demand/building_stock_generator.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,7 @@
""" Building Generator """
import sys
import os
from pprint import pprint
import building_stock_functions as bf
from main_functions import read_csv
import numpy as np
""" Building Generator"""
# pylint: disable=I0011,C0321,C0301,C0103, C0325

import energy_demand.building_stock_functions as bf

def virtual_building_stock(data, assumptions, data_ext):
"""Creates a virtual building stock based on base year data and assumptions
Expand All @@ -31,88 +26,74 @@ def virtual_building_stock(data, assumptions, data_ext):
Notes
-----
The header row is always skipped.
# TODO: check base year run...
Needs as an input all population changes up to simulation period....(to calculate built housing)
"""
uniqueID = 100000 # Initialise
reg_building_stock = {}
reg_building_stock_by = {}
uniqueID, reg_building_stock_cur_yr, reg_building_stock_by = 100000, {}, {}

# Base year data
base_year = data['global_variables']['base_year']
reg_pop = data['reg_pop']

base_year = data['glob_var']['base_year']
dwtype_distr_by = data['dwtype_distr'][base_year] # Distribution of dwelling types 2015.0: {'semi_detached': 26.0, 'terraced': 28.3, 'flat': 20.3, 'detached': 16.6, 'bungalow': 8.8}
dwtype_age_distr_by = data['dwtype_age_distr'][base_year] # Age distribution of dwelling types {2015: {1918: 20.8, 1928: 36.3, 1949: 29.4, 1968: 8.0, 1995: 5.4}} # year, average_age, percent
dwtype_floor_area = data['dwtype_floor_area'] # Floor area [m2] {'semi_detached': 96.0, 'terraced': 82.5, 'flat': 61.0, 'detached': 147.0, 'bungalow': 77.0}
reg_floor_area = data['reg_floor_area'] # Floor Area in a region
dwtype_age_distr_by = data['dwtype_age_distr'][base_year] # Age distribution of dwelling types {2015: {1918: 20.8, 1928: 36.3, 1949: 29.4, 1968: 8.0, 1995: 5.4}} # year, average_age, percent
#dwtype_floor_area = data['dwtype_floor_area'] # Floor area [m2] {'semi_detached': 96.0, 'terraced': 82.5, 'flat': 61.0, 'detached': 147.0, 'bungalow': 77.0}
#reg_floor_area = data['reg_floor_area'] # Floor Area in a region
dw_lu = data['dwtype_lu']
global_variables = data['global_variables']
glob_var = data['glob_var']
reg_lu = data['reg_lu'] # Regions

# External data (current year)
reg_pop_cy = data_ext['population']


# ----- Building stock scenario assumptions
data_floor_area_pp_sim = bf.get_floor_area_pp(reg_floor_area, reg_pop_cy, global_variables, assumptions['change_floor_area_pp']) # Calculate floor area per person over simulation period
dwtype_distr_sim = bf.get_dwtype_dist(dwtype_distr_by, assumptions['assump_dwtype_distr'], global_variables) # Calculate distribution of dwelling types over simulation period
data_floor_area_pp_sim = bf.get_floor_area_pp(data['reg_floor_area'], reg_pop_cy, glob_var, assumptions['change_floor_area_pp']) # Calculate floor area per person over simulation period
dwtype_distr_sim = bf.get_dwtype_dist(dwtype_distr_by, assumptions['assump_dwtype_distr'], glob_var) # Calculate distribution of dwelling types over simulation period
##dwtype_age_distr_sim = bf.get_dwtype_age_distr(dwtype_age_distr) ## Assume that alway the same for base building stock

# Iterate regions
for reg_id in reg_lu:
dw_stock_old, dw_stock_new = [], [] # Initialise

# Read base year data
floor_area_by = reg_floor_area[reg_id] # Read in floor area
dw_nr = data['reg_dw_nr'][reg_id] # Read in nr of dwellings
floor_area_by = data['reg_floor_area'][reg_id] # Read in floor area
dw_nr_by = data['reg_dw_nr'][reg_id] # Read in nr of dwellings

# Percent of floor area of base year (could be changed)
floor_area_p_by = p_floor_area_dwtype(dw_lu, dwtype_distr_by, dwtype_floor_area, dw_nr) # Sum must be 0
floor_area_p_by = p_floor_area_dwtype(dw_lu, dwtype_distr_by, data['dwtype_floor_area'], dw_nr_by) # Sum must be 0

# --Scenario drivers
# base year
pop_by = reg_pop[reg_id] # Read in population
pop_by = data['reg_pop'][reg_id] # Read in population
floor_area_pp_by = floor_area_by / pop_by # Floor area per person [m2/person]
floor_area_by_pd = floor_area_by / dw_nr # Floor area per dwelling [m2/dwelling]
floor_area_by_pd = floor_area_by / dw_nr_by # Floor area per dwelling [m2/dwelling]

# Current year of simulation
pop_cy = reg_pop_cy[reg_id] # Read in population for current year
floor_area_pp_cy = data_floor_area_pp_sim[reg_id][global_variables['current_year']] # Read in from simulated floor_area_pp dict for current year
floor_area_pp_cy = data_floor_area_pp_sim[reg_id][glob_var['current_year']] # Read in from simulated floor_area_pp dict for current year
floor_area_by_pd_cy = floor_area_by_pd ### #TODO:floor area per dwelling get new floor_area_by_pd (if not constant over time, cann't extrapolate for any year)

# Population
#pop_new_dw = pop_cy - pop_by

# Total new floor area
tot_floor_area_cy = floor_area_pp_cy * pop_cy
tot_new_floor_area = tot_floor_area_cy - floor_area_by # tot new floor area - area base year = New needed floor area

# Calculate total number of dwellings (existing build + need for new ones)
dw_new = tot_new_floor_area / floor_area_by_pd_cy # Needed new buildings (if not constant, must calculate nr of buildings in every year)
total_nr_dw = dw_nr + dw_new # floor area / buildings
total_nr_dw = dw_nr_by + dw_new # floor area / buildings

print("New buildings: " + str(dw_new))
print("total_nr_dw: " + str(total_nr_dw))
# Dwelling stock base year
dw_stock_base = generate_dwellings_distr(uniqueID, dw_lu, floor_area_p_by, floor_area_by, dwtype_age_distr_by, floor_area_pp_by, tot_floor_area_cy, pop_by)

# ---- base year DONE
dw_stock_base = generate_dwellings_distribution(uniqueID, dw_lu, dwtype_distr_by, floor_area_p_by, floor_area_by, dwtype_age_distr_by, floor_area_pp_by, tot_floor_area_cy, pop_by)
# Dwelling stock current year
# Existing buildings
dw_stock_cur_old = generate_dwellings_distr(uniqueID, dw_lu, floor_area_p_by, floor_area_by, dwtype_age_distr_by, floor_area_pp_cy, tot_floor_area_cy, tot_floor_area_cy/floor_area_pp_cy)

# Current Year Building stock
# ---- old buildings
dw_stock_cur_old = generate_dwellings_distribution(uniqueID, dw_lu, dwtype_distr_by, floor_area_p_by, floor_area_by, dwtype_age_distr_by, floor_area_pp_cy, tot_floor_area_cy, tot_floor_area_cy/floor_area_pp_cy)

# ------------ new buildings
# New buildings
dw_stock_cur_new = []
for dw in dw_lu:
dw_type_id, dw_type_name = dw, dw_lu[dw]

# Iterate simulation years

# ask will
## Get distribution for every year dwtype_distr_sim
## Get floor area for every year

## Get floor area & nr of dwellings for every year

percent_dw_type = dwtype_distr_by[dw_type_name] / 100 # Percentage of dwelling type (TODO: Nimm scneario value)
#print("Floor area of new buildings per dwelling types: " + str(tot_new_floor_area))

dw_type_dw_nr_new = percent_dw_type * dw_new # Nr of existing dwellings dw type new
#print("Nr of Dwelling of tshi type: " + str(dw_type_dw_nr_new))
Expand All @@ -123,17 +104,14 @@ def virtual_building_stock(data, assumptions, data_ext):
# Pop
pop_dwtype_dw_type = dw_type_floor_area / floor_area_pp_cy # Distribed with floor area..could do better

_scrap_pop_new += pop_dwtype_dw_type
_scrap_floor_new += dw_type_floor_area

# Get age of building construction
sim_period = range(global_variables['base_year'], global_variables['current_year'] + 1, 1) #base year, current year + 1, iteration step
sim_period = range(glob_var['base_year'], glob_var['current_year'] + 1, 1) #base year, current year + 1, iteration step
nr_sim_y = len(sim_period)

for simulation_year in sim_period:

# --- create building object
dw_stock_new.append(bf.Dwelling(['X', 'Y'], dw_type_id, uniqueID, simulation_year, pop_dwtype_dw_type/nr_sim_y, dw_type_floor_area/nr_sim_y, 9999))
# create dwelling object
dw_stock_cur_new.append(bf.Dwelling(['X', 'Y'], dw_type_id, uniqueID, simulation_year, pop_dwtype_dw_type/nr_sim_y, dw_type_floor_area/nr_sim_y, 9999))
uniqueID += 1

# Test if disaggregation was good (do some tests # Todo)
Expand All @@ -150,27 +128,22 @@ def virtual_building_stock(data, assumptions, data_ext):
print("Floor area of current year: " + str(tot_floor_area_cy))
'''
# Generate region and save it in dictionary
reg_building_stock[reg_id] = bf.BuildingStockRegion(reg_id, dw_stock_old + dw_stock_new) # Add old and new buildings to stock
reg_building_stock_by[reg_id] = bf.BuildingStockRegion(reg_id, dw_stock_base) # Add base year stock
reg_building_stock_cur_yr[reg_id] = bf.DwStockRegion(reg_id, dw_stock_cur_old + dw_stock_cur_new) # Add old and new buildings to stock
reg_building_stock_by[reg_id] = bf.DwStockRegion(reg_id, dw_stock_base) # Add base year stock

# Access list: reg_building_stock_by[reg_id].dwellings
print("dd")
for i in reg_building_stock_by[reg_id].dwellings:
print (i.__dict__)
print(reg_building_stock_by[reg_id].get_tot_pop())
#print("dd")
#for i in reg_building_stock_by[reg_id].dwellings:
# print (i.__dict__)
#print(reg_building_stock_by[reg_id].get_tot_pop())

#prnt("..")
#print(reg_building_stock[0].dwelling_list)
#l = reg_building_stock[0].dwelling_list
for i in l:
print(i.__dict__)
#for i in l:
# print(i.__dict__)
return reg_building_stock_by, reg_building_stock_cur_yr


return dw_stock_old, dw_stock_new



# -----------Calculate the share of total floor area belonging to each dwelling type-----------
def p_floor_area_dwtype(dw_lookup, dw_dist, dw_floor_area, dw_nr):
""" Calculates the percentage of floor area belonging to each dwelling type
depending on average floor area per dwelling type
Expand Down Expand Up @@ -205,22 +178,15 @@ def p_floor_area_dwtype(dw_lookup, dw_dist, dw_floor_area, dw_nr):

return dw_floor_area_p

def generate_dwellings_distr(uniqueID, dw_lu, floor_area_p_by, floor_area_by, dwtype_age_distr_by, floor_area_pp_by, tot_floor_area_cy, pop_by):
"""Generates dwellings according to age, floor area and distribution assumptsion"""



def generate_dwellings_distribution(uniqueID, dw_lu, dwtype_distr_by, floor_area_p_by, floor_area_by, dwtype_age_distr_by, floor_area_pp_by, tot_floor_area_cy, pop_by):
"""GEnerates dwellings according to age, floor area and distribution assumptsion"""

dw_stock_base = []

control_pop = 0
control_floor_area = 0
dw_stock_base, control_pop, control_floor_area = [], 0, 0

# Iterate dwelling types
for dw in dw_lu:
dw_type_id, dw_type_name = dw, dw_lu[dw]
percent_dw_type = dwtype_distr_by[dw_type_name] / 100 # Percentage of dwelling type
dw_type_floor_area = floor_area_p_by[dw_type_name] * floor_area_by # Floor areay of existing buildlings
dw_type_floor_area = floor_area_p_by[dw_type_name] * floor_area_by # Floor area of existing buildlings

# Distribute according to age
for dwtype_age_id in dwtype_age_distr_by:
Expand All @@ -239,64 +205,6 @@ def generate_dwellings_distribution(uniqueID, dw_lu, dwtype_distr_by, floor_area
uniqueID += 1

assert round(tot_floor_area_cy, 3) == round(control_floor_area, 3) # Test if floor area are the same
print(pop_by)
print(control_pop)
assert round(pop_by, 3) == round(control_pop, 3) # Test if pop is the same

return dw_stock_base




"""
# (1) Distribution of dwellings per type
# (2) Age distribution of dwellings
# (3) Average floor area per type
# (4) Renovations?
# (5) New houses
dwelling_number = 1000
house_id = ""
coordinates = []
age = age
self.hlc = hlc
self.pop = pop
self.floor_area = floor_area
self.temp = temp
#Spatial unit
-Make possible also for postcodes...
# Information about distribution of buildints
#Steps
1. Calculate base stock
2. Calculate new building stock based on demographics
3.
# Input from real world
# ---------------------
- nr_dwellings
- tot_floor_area_cy
- distribution of dwelling types
- total population
- XY coordinates
- Pop_ID
-
"""







#
"""
__dict__
"""
#a = Town_region("Testname", 10).S_D

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