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set_cases_cpeq_extra.py
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import datetime
import json
import os
import pandas as pd
import dict_update
import sample_gen
import idf_creator_floor as whole_gen
import singlezone_diss
import runep_subprocess
import output_processing2
import other_crack_fac
update = dict_update.update
# Globals
FOLDER = 'cp_eq_extra'
SIZE = 200
SAMPLE_NAME = 'sample_cpeq'
NUM_CLUSTERS = int(os.cpu_count()/2)
NAME_STDRD = 'whole'
NAME_STDRD_2 = 'single'
INPUT = "seed.json" # INPUT_WHOLE
# INPUT_SZ = "seed_sz.json"
EXTENSION = 'epJSON'
REMOVE_ALL_BUT = [EXTENSION, 'csv', 'err']
EPW_NAME = '~/dissertacao/BRA_SP_Sao.Paulo-Congonhas.AP.837800_TMYx.2003-2017.epw'
MONTH_MEANS = '/media/marcelo/OS/LabEEE_1-2/idf-creator/month_means_8760.csv'
OUTPUT_PROCESSED = 'means_'+FOLDER
CONCRETE_EPS = True
SOBOL = False
PARAMETERS = {
'area':[20,100],
'ratio':[.4,2.5],
'zone_height':[2.3,3.2],
'azimuth':[0,359.9],
'floor_height':[0,50],
'absorptance':[.2,.8],
'wall_u':[.5,4.4],
'wall_ct':[.22,450],
'wwr':[.1,.6],
'glass':[.2,.87],
'shading':[0,80],
'people':[.05,.2],
'corner_window':[0,1],
'open_fac':[0.2,1],
'roof':[0,1],
'ground':[0,1],
'bldg_ratio': [.2,1],
# 'n_floor':[1,9],
'v_ar':[0,1]
}
start_time = datetime.datetime.now()
# Dependents
col_names = list(PARAMETERS)
samples_x_cluster = SIZE/NUM_CLUSTERS
name_length = '{:0'+str(len(str(SIZE)))+'.0f}'
name_length_cluster = '{:0'+str(len(str(NUM_CLUSTERS)))+'.0f}'
def add_crack(file_name, crack_fac=.1):
with open(file_name, 'r') as file:
model = json.loads(file.read())
model["AirflowNetwork:MultiZone:Surface:Crack"] = {
"door_crack": {
"air_mass_flow_coefficient_at_reference_conditions": crack_fac,
"air_mass_flow_exponent": 0.667,
"idf_max_extensible_fields": 0,
"idf_max_fields": 4
}
}
with open(file_name, 'w') as file:
file.write(json.dumps(model))
def parameter_value(key, i):
value = PARAMETERS[key][0]+(PARAMETERS[key][1]-PARAMETERS[key][0])*i
return value
print('\nCREATING DIRECTORIES\n')
os.system('mkdir '+FOLDER)
for i in range(NUM_CLUSTERS):
os.system('mkdir '+FOLDER+'/cluster'+name_length_cluster.format(i))
# Generate sample
print('\nGENERATING SAMPLE\n')
# sample = sample_gen.main(SIZE, col_names, SAMPLE_NAME, sobol=False)
sample = pd.read_csv(SAMPLE_NAME+'.csv')
if SOBOL:
sample = (sample+1)/2
# Set cases
print('\nGENERATING MODELS\n')
df = pd.DataFrame(columns=col_names+['folder','file'])
line = 0
for i in range(len(sample)):
sample_line = list(sample.iloc[i])
model_values = dict((param,parameter_value(param, sample.loc[i, param])) for param in col_names)
corr_width = 2
if model_values['roof'] > .5:
roof = True
else:
roof = False
if model_values['ground'] > .5:
ground = True
else:
ground = False
if model_values['corner_window'] > .5:
corner_window = True
else:
corner_window = False
zone_feat = whole_gen.zone_list(model_values)
cluster_n = int(line//samples_x_cluster)
case = name_length.format(line)
output = (FOLDER+'/cluster'+name_length_cluster.format(cluster_n)+'/'+NAME_STDRD+'_{}.epJSON'.format(case))
df = df.append(pd.DataFrame([sample_line+['cluster'+name_length_cluster.format(cluster_n),NAME_STDRD+'_{}.epJSON'.format(case)]],columns=col_names+['folder','file']))
# print(output)
# whole_gen.main(
# zone_area = model_values['area'],
# zone_ratio = model_values['ratio'],
# zone_height = model_values['zone_height'],
# absorptance = model_values['absorptance'],
# shading = model_values['shading'],
# azimuth = model_values['azimuth'],
# corr_width = corr_width,
# wall_u = model_values['wall_u'],
# wall_ct = model_values['wall_ct'],
# corr_vent = 1,
# stairs = 0,
# zone_feat = zone_feat,
# concrete_eps=CONCRETE_EPS,
# zones_x_floor = 6,
# n_floors = 1,
# corner_window=corner_window,
# ground=ground,
# roof=roof,
# floor_height = model_values['floor_height'],
# input_file = INPUT,
# output = output
# )
azimuth_left = (model_values['azimuth']+270)%360
azimuth_right = (model_values['azimuth']+90)%360
for i in range(6):
if i%2 == 0:
azi = azimuth_left
else:
azi = azimuth_right
output = (FOLDER+'/cluster'+name_length_cluster.format(cluster_n)+'/'+NAME_STDRD_2+'_noeq_01'+'_{}_'.format(case)+str(i)+'.epJSON')
df = df.append(pd.DataFrame([sample_line+['cluster'+name_length_cluster.format(cluster_n),NAME_STDRD_2+'_noeq_01'+'_{}_'.format(case)+str(i)+'.epJSON'.format(case)]],columns=col_names+['folder','file']))
# singlezone_diss.main(
# zone_area = model_values['area'],
# zone_ratio = model_values['ratio'],
# zone_height = model_values['zone_height'],
# absorptance = model_values['absorptance'],
# shading = model_values['shading'],
# azimuth = azi,
# bldg_ratio = model_values['bldg_ratio'],
# wall_u = model_values['wall_u'],
# wall_ct = model_values['wall_ct'],
# zn=i,
# floor_height = model_values['floor_height'],
# corner_window = corner_window,
# ground=ground,
# roof=roof,
# people=model_values['people'],
# glass_fs=model_values['glass'],
# wwr=model_values['wwr'],
# door=False,
# cp_eq = False,
# open_fac=model_values['open_fac'],
# input_file=INPUT ,
# output=output,
# outdoors=False
# )
# add_crack(output, .01)
output = (FOLDER+'/cluster'+name_length_cluster.format(cluster_n)+'/'+NAME_STDRD_2+'_cpeq_01'+'_{}_'.format(case)+str(i)+'.epJSON')
df = df.append(pd.DataFrame([sample_line+['cluster'+name_length_cluster.format(cluster_n),NAME_STDRD_2+'_cpeq_01'+'_{}_'.format(case)+str(i)+'.epJSON'.format(case)]],columns=col_names+['folder','file']))
# singlezone_diss.main(
# zone_area = model_values['area'],
# zone_ratio = model_values['ratio'],
# zone_height = model_values['zone_height'],
# absorptance = model_values['absorptance'],
# shading = model_values['shading'],
# azimuth = azi,
# bldg_ratio = model_values['bldg_ratio'],
# wall_u = model_values['wall_u'],
# wall_ct = model_values['wall_ct'],
# zn=i,
# floor_height=model_values['floor_height'],
# corner_window=corner_window,
# ground=ground,
# roof=roof,
# people=model_values['people'],
# glass_fs=model_values['glass'],
# wwr=model_values['wwr'],
# door=False,
# cp_eq = True,
# open_fac=model_values['open_fac'],
# input_file=INPUT,
# output=output,
# outdoors=False
# )
# add_crack(output, .01)
line += 1
df = other_crack_fac.main(df,folder=FOLDER, pattern='_01_')
os.chdir(FOLDER)
# print('\nRUNNING SIMULATIONS\n')
# list_epjson_names = runep_subprocess.gen_list_epjson_names(NUM_CLUSTERS, EXTENSION)
# runep_subprocess.main(list_epjson_names, NUM_CLUSTERS, EXTENSION, REMOVE_ALL_BUT, epw_name=EPW_NAME)
print('\nPROCESSING OUTPUT\n')
output_processing2.main(df, MONTH_MEANS, OUTPUT_PROCESSED)
end_time = datetime.datetime.now()
total_time = (end_time - start_time)
print("Total processing time: " + str(total_time))