def process_reinfections_report(sim_dir_map, sim_id):
survey_report_output = {}
reinfections = {}
for i,day in enumerate(campaign_days):
# get reports before and after campaign
survey_day_prior = day - 5
survey_day_after = day + 5
survey_report_output[survey_day_after] = {}
survey_report_prior_path = os.path.join(sim_dir_map[sim_id],'output', 'MalariaSurveyJSONAnalyzer_Day_' + str(survey_day_prior) + '_0.json')
survey_report_after_path = os.path.join(sim_dir_map[sim_id],'output', 'MalariaSurveyJSONAnalyzer_Day_' + str(survey_day_after) + '_0.json')
survey_report_prior = json2dict(survey_report_prior_path)
survey_report_after = json2dict(survey_report_after_path)
# debug statements
#print len(survey_report_prior['patient_array'])
#print len(survey_report_after['patient_array'])
# re-index reports data by patient id
survey_report_prior_reindexed = {}
# index patient by id which perhaps is a bit more sensible than the current reporter output
for patient in survey_report_prior['patient_array']:
survey_report_prior_reindexed[patient['id']] = patient
survey_report_after_reindexed = {}
for patient in survey_report_after['patient_array']:
survey_report_after_reindexed[patient['id']] = patient
survey_report_prior = survey_report_prior_reindexed
survey_report_after = survey_report_after_reindexed
# determine if each patient in report is reinfected for each pair of consecutive surveys
total_tested_patients = 0
num_reinfected = 0
count_not_found = 0
for patient_id, patient in survey_report_prior.iteritems():
treatment_prior = ""
for drugs in patient['treatment'][0]:
if drugs != "":
treatment_prior = drugs
if patient_id in survey_report_after:
patient_after = survey_report_after[patient_id]
else:
continue
treatment_after = ""
for drugs in patient_after['treatment'][0]:
if drugs != "":
treatment_after = drugs
if ' + '+treatment_prior in treatment_after and treatment_prior != '':
treatment_after = string.replace(treatment_after, ' + ' + treatment_prior, '', maxreplace = 1)
else:
treatment_after = string.replace(treatment_after, treatment_prior, '', maxreplace = 1)
#print "clean after treatment " + str(treatment_after)
survey_report_output[survey_day_after][patient_id] = {}
survey_report_output[survey_day_after][patient_id]['initial_age'] = patient['initial_age']
survey_report_output[survey_day_after][patient_id]['treatment'] = treatment_after
if i > 0:
survey_day_1 = campaign_days[i - 1] + 5
survey_day_2 = survey_day_after
if patient_id in survey_report_output[survey_day_1]:
treatment_1 = survey_report_output[survey_day_1][patient_id]['treatment']
treatment_2 = treatment_after
if treatment_1 != '' and treatment_2 != '':
total_tested_patients = total_tested_patients + 1
# DO NOT APPLY FOR MDA CAMAPIGNS WITHOUT MODIFICATION!!!!!!
if ('Artemether' in treatment_1) and ('Artemether' in treatment_2):
num_reinfected = num_reinfected + 1
'''
# for MDA do something along the lines of
if (('DHA' in patient['treatment'] and 'Vehicle' in patient['treatment']) or 'Artemether' in patient['treatment']) and\
(('DHA' in survey_report_output[second_survey_day][patient_id]['treatment'] and 'Vehicle' in survey_report_output[second_survey_day][patient_id]['treatment']) or 'Artemether' in survey_report_output[second_survey_day][patient_id]['treatment']):
num_reinfected = num_reinfected + 1
'''
else:
#print "patient " + str(patient_id) + " not found in second_survey day (" + str(second_survey_day) + ") report"
count_not_found = count_not_found + 1
if i > 0:
if total_tested_patients != 0:
reinfections['round_' + str(i) + '_' + str(i+1)] = num_reinfected/(total_tested_patients + 0.0)
else:
reinfections['round_' + str(i) + '_' + str(i+1)] = 'nan'
return reinfections
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
import pandas as pd
import json
from nodenames import *
from dtk.utils.parsers.json2dict import json2dict
import operator
net_info_file = "prevalence_net-use_service-use_by_new_cluster_waves1-6.csv"
nets_calib = json2dict("cluster_tags_net_usage_single_node_updated.json")
df = pd.io.parsers.read_csv(net_info_file)
grouped_rounds = df.groupby("cluster.id")
net_usage = {}
for cluster_id, group in grouped_rounds:
pop_cluster = group["pop"].to_dict()
net_usage_cluster = group.UsedLastNight.to_dict()
for k, v in net_usage_cluster.iteritems():
if str(v) == "nan":
vals = np.array(net_usage_cluster.values())
net_usage_cluster[k] = vals[~np.isnan(vals)].mean()
rounds_idx = group.round.to_dict()
for k, v in rounds_idx.iteritems():
net_usage_cluster[str(v)] = net_usage_cluster.pop(k)
pop_cluster[str(v)] = pop_cluster.pop(k)
rnd_max_pop = max(pop_cluster.iteritems(), key=operator.itemgetter(1))[0]
