num_sample = 1
#1.sampleing: get different value of model input (LHM)
import sampleMeta as samp
for cz in climate:
data_set, param_values = samp.sampleMeta(num_sample, cz)
# data_set contains variables name, min value, max value in climate zone cz
#param_values is the sample which contain the vaiables' value
## record the data in the folder './results/samples'
## store the information of data_set
with open('./results/samples/data_set_' + cz + '.csv', 'wb') as csvfile:
for row in data_set:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
## store the information of param_values
with open('./results/samples/param_values_' + cz + '.csv',
'wb') as csvfile:
for row in param_values:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
###################################################################################
#2.modify IDF file and run model, get model output (site EUI)s
###model inputs and outputs are saved in './results/energy_data.csv'
import parallelSimuMeta as ps
for cz in climate:
model_results, run_time = ps.parallelSimu(cz, 1)
rmtree('./Model/update_models')
print run_time
for row in data_set:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
## store the information of param_values
with open('./results/samples/param_values_'+str(cz)+'.csv', 'wb') as csvfile:
for row in param_values:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
# run the models
os.chdir(os.path.join(pathway,'runModel'))
import parallelSimuMeta as ps
os.chdir(pathway)
model_results,run_time = ps.parallelSimu(climate,1)
print run_time
###############################################################################
# sensitivity analysis
## method description
#### 1-Morris:
###### sample: from SALib.sample import morris
###### analyze: from SALib.analyze import morris
#### 2-FAST:
###### sample: from SALib.sample import fast_sampler
###### analyze: from SALib.analyze import fast
data.writerow(row)
## store the information of param_values
with open('./results/samples/param_values_' + str(cz) + '.csv',
'wb') as csvfile:
for row in param_values:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
# run the models
os.chdir(os.path.join(pathway, 'runModel'))
import parallelSimuMeta as ps
os.chdir(pathway)
model_results, run_time = ps.parallelSimu(climate, 1)
print run_time
'''
###############################################################################
# sensitivity analysis
## method description
#### 1-Morris:
###### sample: from SALib.sample import morris
###### analyze: from SALib.analyze import morris
#### 2-FAST:
###### sample: from SALib.sample import fast_sampler
###### analyze: from SALib.analyze import fast
for row in data_set:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
## store the information of param_values
with open('./results/samples/param_values.csv', 'wb') as csvfile:
for row in param_values:
data = csv.writer(csvfile, delimiter=',')
data.writerow(row)
###################################################################################
#2.modify IDF file and run model, get model output (site EUI)s
###model inputs and outputs are saved in './results/energy_data.csv'
import parallelSimuMeta as ps
os.chdir(pathway)
for cz in climate:
model_results,run_time = ps.parallelSimu(cz,1,'pre')
model_results,run_time = ps.parallelSimu(cz,1,'post')
print run_time
###################################
#choose the best operation hour
energy_data_pre=[]
with open('./results/energy_data_pre.csv', 'rb') as csvfile:
data = csv.reader(csvfile, delimiter=',')
for row in data:
energy_data_pre.append(row)
energy_data_post=[]
with open('./results/energy_data_post.csv', 'rb') as csvfile:
data = csv.reader(csvfile, delimiter=',')
