def SA_tx_att_hrg(values):
replace_var = ['TransmissionRateOnART', 'TransmissionRateOffART', 'DynamicTransmissionPropHRGAttrib']
replace_val = {}
for city in ['rio', 'salvador', 'manaus']:
for k in values:# this is to vary % on ART
# multiply to attia
attia_on_ART = np.multiply(parameters['attia on ART'], 1)
attia_off_ART = np.multiply(parameters['attia off ART'], 1)
replace_val['TransmissionRateOnART'] = np.multiply(np.reshape(attia_on_ART, (8, 1)), np.ones((8, 6)))
replace_val['TransmissionRateOffART'] = np.multiply(np.reshape(attia_off_ART, (8, 1)), np.ones((8, 6)))
# the percentage attributable to hrg
replace_val['DynamicTransmissionPropHRGAttrib'] = k
# replace values in .in file
float_df = deepcopy(in_file[city])
# replace the variables
for var in replace_var:
float_df = t_op.replace_values(var, replace_val[var], float_df)
# save float_df
filename = os.path.join(filepath_new, city + '_att_to_hrg_' + str(k) + '.in')
link.write_cepac_in_file(filename, float_df)
# parallelize files
c_op.parallelize_input(filepath_new)
return
def SA_testing_rate(values):
replace_var = ['HIVtestBgAcceptRate']
replace_val = {}
for city in ['rio', 'salvador', 'manaus']:
for k in values:# this is to vary % on ART
# dict of values
replace_val['HIVtestBgAcceptRate'] = k
# replace values in .in file
float_df = deepcopy(in_file[city])
# replace the variables
for var in replace_var:
float_df = t_op.replace_values(var, replace_val[var], float_df)
# save float_df
filename = os.path.join(filepath_new, city + '_testing_rate_' + str(k) + '.in')
link.write_cepac_in_file(filename, float_df)
# parallelize files
c_op.parallelize_input(filepath_new)
return
def SA_testing(values):
# variables to alter and their values
replace_var = ['TransmissionRateOnART', 'TransmissionRateOffART', 'HIVtestBgAcceptRate'] #
replace_val = {}
# multiply to attia
attia_new = np.multiply(parameters['attia'], 1)
replace_val['TransmissionRateOnART'] = np.multiply(np.reshape(attia_new, (8, 1)), np.ones((8, 6)))
replace_val['TransmissionRateOffART'] = np.multiply(np.reshape(attia_new, (8, 1)), np.ones((8, 6)))
for city in ['rio', 'salvador', 'manaus']:
for k in values:
# change background testing rate
replace_val['HIVtestBgAcceptRate'] = k
# replace values in .in file
float_df = deepcopy(in_file[city])
# replace the variables
for var in replace_var:
float_df = t_op.replace_values(var, replace_val[var], float_df)
# save float_df
filename = os.path.join(filepath_new, city + '_Test accept rate_' + str(k) + '.in')
link.write_cepac_in_file(filename, float_df)
# parallelize files
c_op.parallelize_input(filepath_new)
return
def SA_acute(values):
# loop over scale down scenarios
replace_var = ['TransmissionRateOnART', 'TransmissionRateOffART']#, 'TransmissionRateOnART', 'TransmissionRateOffART']
replace_val = {}
for city in ['rio', 'salvador', 'manaus']:
for k in values:
# multiply to attia
attia_new = parameters['attia']
attia_new[len(attia_new)-1] = k
replace_val['TransmissionRateOnART'] = np.multiply(np.reshape(attia_new, (8, 1)), np.ones((8, 6)))
replace_val['TransmissionRateOffART'] = np.multiply(np.reshape(attia_new, (8, 1)), np.ones((8, 6)))
# replace values in .in file
float_df = deepcopy(in_file[city])
# replace the variables
for var in replace_var:
float_df = t_op.replace_values(var, replace_val[var], float_df)
# save float_df
filename = os.path.join(filepath_new, city + '_Acute rate_' + str(k) + '.in')
link.write_cepac_in_file(filename, float_df)
# parallelize files
c_op.parallelize_input(filepath_new)
def create_in_files(self, parallel = 1):
# get variable list, value map and base in_file
var_list = self.var_list
var0, var1 = var_list[0], var_list[1]
val_map = self.val_map
in_file = deepcopy(self.base_in_file)
# first start loop over var1 values
for var0_val in val_map[var0]:
# this val can either be a float or array
if not (isinstance(var0_val, float) or isinstance(var0_val, int) or isinstance(var0_val, np.array)):
raise TypeError('The value of variable for TWSA must be np.array/float/int')
return
# replace variable value
in_file = t_op.replace_values(var0, var0_val, in_file, position = self.var_pos[var0])
# TODO: find and replace dependent variables of var0
in_file = self.dep_data_object.replace_dep_value(var0, var0_val, self.dep_data_map, in_file, self.var_pos)
# now start loop over var2
for var1_val in val_map[var1]:
# this val can either be a float or array
if not (isinstance(var0_val, float) or isinstance(var0_val, int) or isinstance(var0_val, np.array)):
raise TypeError('The value of variable for TWSA must be np.array/float/int')
return
# replace variable value
in_file = t_op.replace_values(var1, var1_val, in_file, position = self.var_pos[var1])
# TODO: find and replace dependent variables of var1
in_file = self.dep_data_object.replace_dep_value(var1, var1_val, self.dep_data_map, in_file, self.var_pos)
# save in file
x = ('%s=%.4f_%s=%.4f.in')%(var0, var0_val, var1, var1_val)
file_name = os.path.join(self.save_path, x)
link.write_cepac_in_file(file_name, in_file)
# parallelize
c_op.parallelize_input(self.save_path, parallel)
return
def SA_treatment(values):
# loop over scale down scenarios
replace_var = ['DynamicTransmissionNumTransmissionsHRG', 'TransmissionRiskMultiplier_T1', 'TransmissionRiskMultiplier_T2', 'TransmissionRiskMultiplier_T3', 'TransmissionRateOnART', 'TransmissionRateOffART']
replace_val = {}
for city in ['rio', 'salvador', 'manaus']:
for k in values:# this is to vary % on ART
# multiply to attia
attia_on_ART = np.multiply(parameters['attia on ART'], 1)
attia_off_ART = np.multiply(parameters['attia off ART'], 1)
replace_val['TransmissionRateOnART'] = np.multiply(np.reshape(attia_on_ART, (8, 1)), np.ones((8, 6)))
replace_val['TransmissionRateOffART'] = np.multiply(np.reshape(attia_off_ART, (8, 1)), np.ones((8, 6)))
# calculate community viral load distibution
on_art = np.multiply(k, parameters['viral load distribution on ART'])
off_art = np.multiply((1-k), parameters['viral load distribution off ART'])
community_vl = np.add(on_art, off_art)
# calculate average transmission rate
tx_avg = np.sum(np.multiply(parameters['attia off ART'], community_vl))
replace_val['DynamicTransmissionNumTransmissionsHRG'] = tx_avg
# calculate transmission rate calibrated to the incidence rate
tx = np.multiply(parameters[city]['incidence'], np.divide(parameters[city]['index negative'], parameters[city]['index positive']))
# calculate multiplier
tx_mul = np.divide(tx, tx_avg)
replace_val['TransmissionRiskMultiplier_T1'] = tx_mul[0]
replace_val['TransmissionRiskMultiplier_T2'] = tx_mul[0]
replace_val['TransmissionRiskMultiplier_T3'] = tx_mul[1]
# replace values in .in file
float_df = deepcopy(in_file[city])
# replace the variables
for var in replace_var:
float_df = t_op.replace_values(var, replace_val[var], float_df)
# save float_df
filename = os.path.join(filepath_new, city + '_On treatment_' + str(k) + '.in')
link.write_cepac_in_file(filename, float_df)
# parallelize files
c_op.parallelize_input(filepath_new)
def SA_attia(factors):
# loop over scale down scenarios
replace_var = ['DynamicTransmissionNumTransmissionsHRG', 'TransmissionRiskMultiplier_T1', 'TransmissionRiskMultiplier_T2', 'TransmissionRiskMultiplier_T3', 'TransmissionRateOnART', 'TransmissionRateOffART']
replace_val = {}
for city in ['rio', 'salvador', 'manaus']:
for k in factors:# this us scale down factor for Attia rates
# multiply to attia
attia_new = np.multiply(parameters['attia'], k)
replace_val['TransmissionRateOnART'] = np.multiply(np.reshape(attia_new, (8, 1)), np.ones((8, 6)))
replace_val['TransmissionRateOffART'] = np.multiply(np.reshape(attia_new, (8, 1)), np.ones((8, 6)))
# calculate average transmission rate
tx_avg = np.sum(np.multiply(attia_new, parameters[city]['viral load distribution']))
replace_val['DynamicTransmissionNumTransmissionsHRG'] = tx_avg
# calculate transmission rate calibrated to the incidence rate
tx = np.multiply(parameters[city]['incidence'], np.divide(parameters[city]['index negative'], parameters[city]['index positive']))
# calculate multiplier
tx_mul = np.divide(tx, tx_avg)
replace_val['TransmissionRiskMultiplier_T1'] = tx_mul[0]
replace_val['TransmissionRiskMultiplier_T2'] = tx_mul[0]
replace_val['TransmissionRiskMultiplier_T3'] = tx_mul[1]
# replace values in .in file
float_df = deepcopy(in_file[city])
# replace the variables
for var in replace_var:
float_df = t_op.replace_values(var, replace_val[var], float_df)
# save float_df
filename = os.path.join(filepath_new, city + '_Attia scale down_' + str(k) + '.in')
link.write_cepac_in_file(filename, float_df)
# parallelize files
c_op.parallelize_input(filepath_new)
# read respective .in file
filepath = os.path.join(readpaths[var], file) + '.in'
float_in = link.read_cepac_in_file(filepath)
# replace few values
r_val_map = {'UseHIVIncidReduction': 1,
'HIVIncidReductionStopTime': HORIZON,
'HIVIncidReductionCoefficient': coeff}
for r_var in r_val_map:
#
if not r_var in position:
position[r_var] = t_op.search_var(r_var, float_in)
# replace value
float_in = t_op.replace_values(r_var, r_val_map[r_var], float_in, position[r_var])
# write the file
varval = file.split('=')[1]
addstr = 'OWSA_final_run_var=%s_val=%s'%(var, varval) + '.in'
if not os.path.exists(finalpaths[var]): os.makedirs(finalpaths[var])
savepath = os.path.join(finalpaths[var], addstr)
link.write_cepac_in_file(savepath, float_in)
# parallelize
c_op.parallelize_input(finalpaths[var])
# save the file
name = os.path.join(savepath_cepac, 'sample_' + str(run) + '.in')
link.write_cepac_in_file(name, float_df)
# featurizarion of the .in file
feature_mat = h_fun1.featurize_in_file(samples, sample_bounds, run, SEQ_LEN)
feature_tensor.append(feature_mat)
# print
print(run)
# save condensed cepac input files
CEPAC_input_condensed = CEPAC_input_condensed.to_dict()
h_fun1.dump_json(CEPAC_input_condensed, os.path.join(savepath_rnn, 'CEPAC_input.json'))
# parallelize input files
c_op.parallelize_input(savepath_cepac, parallel = 5)
# save feature tensor
if not os.path.exists(savepath_rnn):
os.makedirs(savepath_rnn)
h_fun1.dump_json(feature_tensor, os.path.join(savepath_rnn, 'RNN_source.json'))
# stop timer
stop = timeit.default_timer()
print('Time: ', stop - start)
# write excel file for the CEPAC output
link.export_output_to_excel(final_path, final_path)
elif os.path.exists(path_dict['output']['intervention']):
# collect parallelized output
try:
c_op.collect_output(
os.path.join(path_dict['output']['intervention']))
except:
pass
# write all final run files
abc_output.write_final_runs(var_to_replace, path_dict, HORIZON)
# paralellize
c_op.parallelize_input(path_dict['output']['final runs'])
else:
# write all abc run files
abc_input.write_abc(var_to_replace, path_dict)
# parallelize
c_op.parallelize_input(path_dict['output']['intervention'])
# compare New CEPAC with Old CEPAC
"""
#
new_path = r'/Users/vijetadeshpande/Downloads/MPEC/Brazil/Rio/ART CHECK1/Compare different ART parameter setting1/2-way SA/Measurement of community benefit_R10/Status quo_R10/SQ.in'
old_path = r'/Users/vijetadeshpande/Downloads/MPEC/Brazil/Rio/2-way SA_10 year/Measurement of community benefit_R10/Status quo_R10/SQ.in'
new_sq = link.read_cepac_in_file(new_path)
old_sq = link.read_cepac_in_file(old_path)
def parallelize(self, parallel = 1):
# parallelize the files
c_op.parallelize_input(self.save_path, parallel)
return
