for zStar in Grid_Values['zStars']:
for zGas in Grid_Values['zGas']:
for ionization_parameter in Grid_Values['u']:
Model_dict[
'Name'] = 'S_Ar_Test_' + 'age' + age + '_zStar' + zStar + '_zGas' + zGas + '_u' + ionization_parameter
Model_dict['age'] = age
Model_dict['zStars'] = zStar
Model_dict['zGas'] = zGas
Model_dict['u'] = ionization_parameter
Model_dict['abundances_command'] = 'abundances H=0.0 He=-1.0'
for key in Solar_abundances:
Model_dict[
'abundances_command'] = '{previous_line} {new_element}={abundance}'.format(
previous_line=Model_dict['abundances_command'],
new_element=key,
abundance=round(
Solar_abundances[key] + nplog10(float(zGas)),
3))
ScriptName = Model_dict['Name'] + '.in'
#Generate the script
ct.popstar_S_Ar_Grid(ScriptFolder, Model_dict)
# ct.popstar_S_Ar_Grid_absAbundances(ScriptFolder, Model_dict)
#Run the cloudy script
ct.run_script(ScriptName, ScriptFolder)
print 'Data treated'
#Generate the scripts with the lines we want to print the flux
ct.lines_to_extract(ScriptFolder)
#Fore the each grid point run a cloudy simulation
Model_dict = OrderedDict()
for age in Grid_Values['age']:
for zStar in Grid_Values['zStars']:
for zGas in Grid_Values['zGas']:
for ionization_parameter in Grid_Values['u']:
Model_dict['Name'] = 'S_Ar_Test_' + 'age'+ age + '_zStar' + zStar + '_zGas' + zGas + '_u' + ionization_parameter
Model_dict['age'] = age
Model_dict['zStars'] = zStar
Model_dict['zGas'] = zGas
Model_dict['u'] = ionization_parameter
Model_dict['abundances_command'] = 'abundances H=0.0 He=-1.0'
for key in Solar_abundances:
Model_dict['abundances_command'] = '{previous_line} {new_element}={abundance}'.format(previous_line = Model_dict['abundances_command'], new_element=key, abundance=round(Solar_abundances[key] + nplog10(float(zGas)), 3))
ScriptName = Model_dict['Name'] + '.in'
#Generate the script
ct.popstar_S_Ar_Grid(ScriptFolder, Model_dict)
# ct.popstar_S_Ar_Grid_absAbundances(ScriptFolder, Model_dict)
#Run the cloudy script
ct.run_script(ScriptName, ScriptFolder)
print 'Data treated'
