def scenario_4b():
print(
"The second set of simulations starts in 2021 and ends in 2023, while the MDA starts in 2021 and ends in 2022."
)
BetFilePath = 'files/InputBet_scen1.csv'
MDAFilePath = 'files/InputMDA_scen4b.csv'
PrevFilePath = 'files/OutputPrev_scen4b.csv'
Trachoma_Simulation(BetFilePath=BetFilePath,
MDAFilePath=MDAFilePath,
PrevFilePath=PrevFilePath,
SaveOutput=False,
OutSimFilePath=None,
InSimFilePath='files/OutputVals_scen4a.p')
def scenario_4a():
print(
"The first set of simulations starts in 2018 and ends in 2020, while the MDA starts in 2019 and ends in 2020."
)
BetFilePath = 'files/InputBet_scen1.csv'
MDAFilePath = 'files/InputMDA_scen4a.csv'
PrevFilePath = 'files/OutputPrev_scen4a.csv'
Trachoma_Simulation(BetFilePath=BetFilePath,
MDAFilePath=MDAFilePath,
PrevFilePath=PrevFilePath,
SaveOutput=True,
OutSimFilePath='files/OutputVals_scen4a.p',
InSimFilePath=None)
def scenario_6c():
print(
"For comparison purposes, the following code generates a third set of simulations starting in 2018 and ending in 2023, with MDA from 2021 to 2023."
)
BetFilePath = 'files/InputBet_scen1.csv'
MDAFilePath = 'files/InputMDA_scen6c.csv'
PrevFilePath = 'files/OutputPrev_scen6c.csv'
Trachoma_Simulation(BetFilePath=BetFilePath,
MDAFilePath=MDAFilePath,
PrevFilePath=PrevFilePath,
SaveOutput=False,
OutSimFilePath=None,
InSimFilePath=None)
def scenario_6b():
print(
"The second set of simulations starts in 2021 and ends in 2023, with MDA also from 2021 to 2023."
)
BetFilePath = 'files/InputBet_scen1.csv'
MDAFilePath = 'files/InputMDA_scen6b.csv'
PrevFilePath = 'files/OutputPrev_scen6b.csv'
Trachoma_Simulation(BetFilePath=BetFilePath,
MDAFilePath=MDAFilePath,
PrevFilePath=PrevFilePath,
SaveOutput=False,
OutSimFilePath=None,
InSimFilePath='files/OutputVals_scen6a.p')
def scenario_3():
print(
"The simulations start in 2019 and end in 2020, while the there is no MDA."
)
BetFilePath = 'files/InputBet_scen3.csv'
MDAFilePath = 'files/InputMDA_scen3.csv'
PrevFilePath = 'files/OutputPrev_scen3.csv'
Trachoma_Simulation(BetFilePath=BetFilePath,
MDAFilePath=MDAFilePath,
PrevFilePath=PrevFilePath,
SaveOutput=False,
OutSimFilePath=None,
InSimFilePath=None)
print("results in files/OutputPrev_scen3.csv")
def run_trachoma(request_hash, params):
print_function = app.logger.info if app.logger is not None else print
disease = 'trachoma'
file_abbrev = file_name_disease_abbreviations[disease]
try:
# fill in params from UI input
iu = params['iu']
country = iu[0:3]
iu_id = iu[3:]
numReps = 200 if params['runs'] > 200 else params['runs']
MDA_Cov = params['coverage']
mda_list = params['mdaRounds']
print_function(
f"running Trachoma simulation for {iu} {country} {iu_id} {numReps} {mda_list}"
)
# source directory info
source_data_path_root = f"diseases/{disease}/source-data"
source_data_gcs_path_root = f"/{bucket_name}/{source_data_path_root}"
source_directory_path = f"{source_data_path_root}/{country}/{iu}"
source_directory_gs_path = f"/{bucket_name}/{source_directory_path}"
# output directory info
output_data_path_root = f"diseases/{disease}/output-data"
output_data_gcs_path_root = f"/{bucket_name}/{output_data_path_root}"
output_directory_path = f"{output_data_path_root}/{country}/{iu}/{request_hash}"
output_directory_gs_path = f"/{bucket_name}/{output_directory_path}"
# generate Input MDA file from input
mda_file_name = f"InputMDA-{request_hash}.csv"
mda_file_gcs_path = f"{gs_prefix}{output_directory_gs_path}/{mda_file_name}"
# Input MDA data
df = pandas.DataFrame.from_records([{
'start_sim_year': 2020,
'end_sim_year': 2030,
'first_mda': '',
'last_mda': '',
'mda_vector': json.dumps(mda_list)
}])
# write Input MDA to file
df.to_csv(mda_file_gcs_path, index=None)
# set up GCS & file paths for simulation
bet_file_gcs_path = f"{gs_prefix}{source_directory_gs_path}/InputBet_{country}{iu_id}.csv"
infect_file_gcs_path = f"{gs_prefix}{output_directory_gs_path}/InfectFile-{request_hash}.csv"
in_sim_file_path = f"{source_directory_path}/OutputVals_{country}{iu_id}.p"
output_prevalence_file_name = f"OutputPrev-{request_hash}.csv"
output_prevalence_blob_path = f"{output_directory_gs_path}/{output_prevalence_file_name}"
output_prevalence_gcs_path = f"{gs_prefix}{output_directory_gs_path}/{output_prevalence_file_name}"
output_prevalence_https_path = f"{https_prefix}{output_directory_gs_path}/{output_prevalence_file_name}"
# make a json file path to summarise it into
summary_json_file_name = f"OutputPrev-{request_hash}-summary.json"
summary_json_gcs_path = f"{output_directory_path}/{summary_json_file_name}"
summary_json_https_path = f"{https_prefix}{output_directory_gs_path}/{summary_json_file_name}"
# put together historical prevalence file paths
historical_prevalence_filename = f"OutputPrev_{country}{iu_id}.csv"
historical_prevalence_gcs_path = f"{gs_prefix}{source_directory_gs_path}/{historical_prevalence_filename}"
historical_prevalence_https_path = f"{https_prefix}{source_directory_gs_path}/{historical_prevalence_filename}"
# check for existing historical prevalence summary
historical_prevalence_summary_filename = f"OutputPrev_{country}{iu_id}-summary.json"
historical_prevalence_summary_blob_path = f"{source_directory_gs_path}/{historical_prevalence_summary_filename}"
historical_prevalence_summary_gcs_path = f"{source_directory_path}/{historical_prevalence_summary_filename}"
historical_prevalence_summary_https_path = f"{https_prefix}{source_directory_gs_path}/{historical_prevalence_summary_filename}"
# stick it all in a dict to save to storage and send to client on success
Result = {
'status': True,
'isNewSimulation': False,
'historicalDataUrl': historical_prevalence_https_path,
'historicalSummaryUrl': historical_prevalence_summary_https_path,
'futureDataUrl': output_prevalence_https_path,
'futureSummaryUrl': summary_json_https_path,
}
if not gcs.blob_exists(output_prevalence_blob_path):
# we're about to kick off a new simulation
Result['isNewSimulation'] = True
# run the simulation
Trachoma_Simulation(
BetFilePath=bet_file_gcs_path,
MDAFilePath=mda_file_gcs_path,
PrevFilePath=output_prevalence_gcs_path,
InfectFilePath=infect_file_gcs_path,
InSimFilePath=in_sim_file_path,
SaveOutput=False,
OutSimFilePath=None,
MDA_Cov=MDA_Cov,
numReps=numReps,
useCloudStorage=True,
logger=app.logger,
download_blob_to_file=gcs.download_blob_to_file,
)
generate_summary(output_prevalence_gcs_path, summary_json_gcs_path)
# generate & write one out if it doesn't exist
if not gcs.blob_exists(historical_prevalence_summary_blob_path):
generate_summary(historical_prevalence_gcs_path,
historical_prevalence_summary_gcs_path)
# snag the output for sending to browser now
output_result_json = json.dumps(Result)
# save result to file for JS to hit next time
ResultJsonFilePath = f"{output_directory_path}/{file_abbrev}-{iu}-{request_hash}-info.json"
Result[
'isNewSimulation'] = False # because reading from static file means it's not new
gcs.write_string_to_file(json.dumps(Result), ResultJsonFilePath)
return Response(output_result_json,
mimetype='application/json; charset=UTF-8')
except Exception as e:
return json.dumps({'status': False, 'msg': str(e)})
prev_csv_file_name = f"{file_name_root}-prev.csv"
PrevFilePath = f"{output_dir}/{prev_csv_file_name}"
PrevCloudPath = f"{cloud_dir}/{prev_csv_file_name}"
infect_csv_file_name = f"{file_name_root}-infect.csv"
InfectFilePath = f"{output_dir}/{infect_csv_file_name}"
InfectCloudPath = f"{cloud_dir}/{infect_csv_file_name}"
print( f"=== Running:\n\tGroup: {group}\n\tMDA_Cov {MDA_Cov}\n\tMDA type {mda_type}\n\tinput {MDAFilePath}\n\toutput {PrevFilePath}\n" )
# run the simulation
Trachoma_Simulation(
BetFilePath=BetFilePath,
MDAFilePath=MDAFilePath,
PrevFilePath=PrevFilePath,
InfectFilePath=InfectFilePath,
SaveOutput=False,
OutSimFilePath=None,
InSimFilePath=InSimFilePath,
MDA_Cov=MDA_Cov
)
# upload prevalence file
prev_blob = bucket.blob( PrevCloudPath )
prev_blob.upload_from_filename( PrevFilePath )
# upload infection file
infect_blob = bucket.blob( InfectCloudPath )
infect_blob.upload_from_filename( InfectFilePath )
# remove the input file
os.remove( MDAFilePath )