-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathset_cases_ann_whole.py
336 lines (274 loc) · 9.72 KB
/
set_cases_ann_whole.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
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_processing
import other_crack_fac
update = dict_update.update
# Globals
FOLDER = 'ann_whole_test' # 'ann_whole_validation' # 'ann_whole' #
SIZE = 5000 # 20000 # 155648
OUTPUT_NAME = 'sample_ann_whole_test' # 'sample_ann_whole_validation' # 'sample_ann_whole' #
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 # True
# CRACK = .8
PARAMETERS = {
'open_fac':[0.2,1],
'v_ar':[0,1],
'corner_window':[0,1], #####
# 'room_type':[0,1],
'area':[20,100],
'people':[.05,.2],
'floor_height':[0,50],
'roof':[0,1],
'shading':[0,80],
'ground':[0,1],
'wall_u':[.5,4.4],
'absorptance':[.2,.8],
'glass':[.2,.87],
'azimuth':[0,359.9],
'wwr':[.1,.6],
# 'n_floor':[1,9], #####
'ratio':[.4,2.5],
'zone_height':[2.3,3.2],
'wall_ct':[.22,450],
# 'bldg_ratio': [.2,1]
}
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, OUTPUT_NAME, sobol=SOBOL)
# sample = pd.read_csv(OUTPUT_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'] > .8:
roof = True
else:
roof = False
if model_values['ground'] > .85:
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+'_{}'.format(case)+'.epJSON')
df = df.append(pd.DataFrame([sample_line+['cluster'+name_length_cluster.format(cluster_n),NAME_STDRD+'_{}'.format(case)+'.epJSON'.format(case)]],columns=col_names+['folder','file']))
print(output)
whole_gen.main(
zone_area = model_values['area'],
zone_ratio = model_values['ratio'], #1, #
zone_height = model_values['zone_height'], # 2.5, #
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'], # 80.5, #
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
)
line += 1
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_processing.main(df, MONTH_MEANS, OUTPUT_PROCESSED)
end_time = datetime.datetime.now()
total_time = (end_time - start_time)
print("Total processing time: " + str(total_time))
########################################################################
os.chdir('/media/marcelo/OS/dissertacao')
# Globals
FOLDER = 'ann_whole_validation' # 'ann_whole_test' # 'ann_whole_validation' #
SIZE = 5000 # 20000 # 155648
OUTPUT_NAME = 'sample_ann_whole_validation' # 'sample_ann_whole' # 'sample_ann_whole_test' #
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 # True
# CRACK = .8
PARAMETERS = {
'open_fac':[0.2,1],
'v_ar':[0,1],
'corner_window':[0,1], #####
# 'room_type':[0,1],
'area':[20,100],
'people':[.05,.2],
'floor_height':[0,50],
'roof':[0,1],
'shading':[0,80],
'ground':[0,1],
'wall_u':[.5,4.4],
'absorptance':[.2,.8],
'glass':[.2,.87],
'azimuth':[0,359.9],
'wwr':[.1,.6],
# 'n_floor':[1,9], #####
# 'ratio':[.4,2.5],
# 'zone_height':[2.3,3.2],
# 'wall_ct':[.22,450],
# 'bldg_ratio': [.2,1]
}
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, OUTPUT_NAME, sobol=SOBOL)
# sample = pd.read_csv(OUTPUT_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'] > .8:
roof = True
else:
roof = False
if model_values['ground'] > .85:
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+'_{}'.format(case)+'.epJSON')
df = df.append(pd.DataFrame([sample_line+['cluster'+name_length_cluster.format(cluster_n),NAME_STDRD+'_{}'.format(case)+'.epJSON'.format(case)]],columns=col_names+['folder','file']))
print(output)
whole_gen.main(
zone_area = model_values['area'],
zone_ratio = 1, # model_values['ratio'],
zone_height = 2.5, # 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 = 80.5, # 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
)
line += 1
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_processing.main(df, MONTH_MEANS, OUTPUT_PROCESSED)
end_time = datetime.datetime.now()
total_time = (end_time - start_time)
print("Total processing time: " + str(total_time))