for sim_id, sim in calib_data.iteritems():
sim_output = sim['output']
x_temp_h = float(sim['meta']['x_Temporary_Larval_Habitat'])
const_h_struct = ast.literal_eval(sim['meta']['scale_larval_habitats_single'])
const_h = const_h_struct[0][1][1]
itn_level_struct = ast.literal_eval(sim['meta']['add_ITN_mult'])
itn_level = itn_level_struct[0][1][0][0][1]
drug_coverage_level_struct = ast.literal_eval(sim['meta']['add_drug_multi_campaigns'])
drug_coverage_level = drug_coverage_level_struct[0][1][0]['coverage']
sim_key = get_sim_key(x_temp_h, const_h, itn_level, drug_coverage_level)
sim_group_key = get_sim_group_key(itn_level, drug_coverage_level)
sim_data = sim_output['Channels']['New Clinical Cases']['Data']
if not sim_group_key in cc:
cc[sim_group_key] = {}
cc[sim_group_key][sim_key] = sim_data
del calib_data
gc.collect()
def get_sweep_results(sim_meta_config_files, calib_file_path, tags_data_file_path):
# Login to COMPs
comps_login()
# find total number of simulations across given experiment files
num_sims = 0
for sim_meta_config_file in sim_meta_config_files:
with open(sim_meta_config_file) as metadata_file:
metadata = json.loads(metadata_file.read())
num_sims = num_sims + len(metadata['sims'])
# Download simulations locally
# sample sim meta config file (like "C:\\Users\\Mnikolov\\Zambia-raw\\dtk-scripts\\1node\\simulations\\Sinamalina_Sinazongwe_Calibration_e9979059-33f8-e411-93f9-f0921c16b9e7.json")
#print 'Downloading simulations from experiment ' + str(sim_meta_config_files) + '...'
# simulations tag data structure: accumulates sims meta information from sims tags
tag_data = {
'ITN trajectory': [],\
'Drug coverage per round': [],\
'Temporary habitat scale': [],\
'Constant habitat scale': []\
}
# iterate through experiments
calib_output = {}
# count processed sims to updated progress
count = 0
for sim_meta_config_file in sim_meta_config_files:
# construct experiment directory structure
with open(sim_meta_config_file) as metadata_file:
metadata = json.loads(metadata_file.read())
output_path = metadata['sim_root']
exp_id = metadata['exp_id']
exp_name = metadata['exp_name']
sim_dir_map = CompsDTKOutputParser.createSimDirectoryMap(exp_id)
# get all successfully completed sims in experiment
for sim_id, sim in metadata['sims'].items():
# get path to the sim timeseries channels data
timeseries_path = os.path.join(sim_dir_map[sim_id],'output', 'InsetChart.json')
#get sim timeseries channels data; json2dict returns None if timeseries_path points to non-existing file, which is the case if the sim has not successfully finished
sim_output = json2dict(timeseries_path)
# only download successfully completed simulations
if sim_output == None:
continue
# delete all but the specified channels
for channel in sim_output['Channels'].keys():
if not channel in channels:
del(sim_output['Channels'][channel])
# process specified reports
report_channels_data = {}
if not reports_channels == None:
report_channels_data = process_reports(reports_channels, sim_dir_map, sim_id)
# record sim meta information including sim tags
tags_path = os.path.join(sim_dir_map[sim_id], 'tags.json')
f = open(tags_path, 'r')
tags = f.read()
sim_meta = ast.literal_eval(tags)
append_tag_data(sim_meta, tag_data)
# construct sim group key and sim key
x_temp_h = sim_meta_2_temp_h(sim_meta)
const_h = sim_meta_2_const_h(sim_meta)
itn_level = sim_meta_2_itn_level(sim_meta)
drug_coverage_level = sim_meta_2_drug_cov(sim_meta)
sim_key = get_sim_key(x_temp_h, const_h, itn_level, drug_coverage_level)
sim_group_key = get_sim_group_key(itn_level, drug_coverage_level)
# store sim channels data
if sim_group_key not in calib_output:
calib_output[sim_group_key] = {}
calib_output[sim_group_key][sim_key] = {
# can add/remove data entries depending on needs
'prevalence': sim_output['Channels']['New Diagnostic Prevalence']['Data'],
'reinfections': report_channels_data['reinfections'],
'meta':sim_meta,
'sim_id':sim_id
}
'''
count = count + 1
percent_complete = 100*count/(num_sims+0.0)
sys.stdout.write('\r')
sys.stdout.write('%2f %%' % percent_complete)
#sys.stdout.write('%d' % count)
sys.stdout.flush()
'''
print ""
print "Writing files..."
with open(calib_file_path, 'w') as calib_f:
json.dump(calib_output, calib_f)
print str(len(calib_output)) + ' simulation results saved to ' + calib_file_path
with open(tags_data_file_path, 'w') as tags_f:
json.dump(tag_data, tags_f)
print 'Meta data tags saved to ' + tags_data_file_path
print ""
return calib_f
