print("Enter the names of the files of spectra, one per line \
(followed by the <RETURN>/<ENTER> key).\n\
When done, press <RETURN> on a blank line to proceed.\n\n\
To specify a path relative to the current directory, \
start the file name with \"" + md.current_dir_id + "\".\n\
You may also provide an absolute path starting with \"" \
+ md.root_dir_id + "\" (root directory)\n\
or with \"" + md.home_dir_id + "\" (home directory).\n\
In all other cases, the path is relative to \"" + md.spectra_dir + "\".\n\
(See variables `root_dir_id`, `home_dir_id`, `home_dir` \
and `spectra_dir`\n\
in file \"mod_directories.py\".)" )
while (True):
row = miu.clean_string(input())
if row == "":
break
else:
file_name.append(md.path_file(md.spectra_dir, row))
pass
#! end if
pass
#! end while
dim_file = len(file_name)
#!======================================================================
#! Read input data:
data = list()
def read_spectra_output(file_name):
#! Beware: Python lists start at index 0 whereas, by default,
#! Fortran arrays start at index 1.
data = struct_spectra_output()
unit = open(file_name)
data.version_id = miu.get_single_string(unit)
data.version_date = miu.get_single_string(unit)
row = miu.next_non_comment_line(unit)
data.spectra_output = miu.clean_string(row.strip().replace('" ', '" , ').split(' , ')[0])
if data.spectra_output == "detailed":
data.RF_output = miu.get_single_boolean(unit)
data.sublim_output = miu.get_single_boolean(unit)
else:
data.RF_output = False
data.sublim_output = False
pass
#! end if
data.time_step = miu.get_single_float(unit)
dim_output_age = miu.get_single_int(unit)
data.dim_output_age = dim_output_age
data.dim_elem = miu.get_single_int(unit)
data.elem_id = np.empty(data.dim_elem, dtype="object")
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
for i_col in range(data.dim_elem):
data.elem_id[i_col] = miu.clean_string(row[i_col])
pass
#! end for
row = unit.readline()
dim_species_SFC = int(row.split()[0])
data.dim_species_SFC = dim_species_SFC
dim_species_DISM = int(row.split()[1])
data.dim_species_DISM = dim_species_DISM
data.species_id_SFC = np.empty(dim_species_SFC, dtype="object")
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
for i_col in range(dim_species_SFC):
data.species_id_SFC[i_col] = miu.clean_string(row[i_col])
pass
#! end for
data.species_id_DISM = np.empty(dim_species_DISM, dtype="object")
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
for i_col in range(dim_species_DISM):
data.species_id_DISM[i_col] = miu.clean_string(row[i_col])
pass
#! end for
dim_cont = miu.get_single_int(unit)
data.dim_cont = dim_cont
dim_line = miu.get_single_int(unit)
data.dim_line = dim_line
miu.skip_row(unit)
data.lambda_cont = np.empty(dim_cont, dtype="float")
i_cont = 0
while True:
row = unit.readline().split()
for i_col in range(len(row)):
data.lambda_cont[i_cont] = float(row[i_col])
i_cont = i_cont+1
pass
#! end for
if i_cont == dim_cont: break
pass
#! end while
miu.skip_row(unit)
data.line_id = np.empty(dim_line, dtype="object")
data.lambda_line = np.empty(dim_line, dtype="float")
for i_row in range(dim_line):
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
data.line_id[i_row] = miu.clean_string(row[0])
data.lambda_line[i_row] = float(row[1])
pass
#! end for
data.reserv_warn_present = miu.get_single_boolean(unit)
data.SF_warn_present = miu.get_single_boolean(unit)
if data.SF_warn_present:
data.SF_warn_age = miu.get_single_float(unit)
pass
#! end if
data.dim_infall_warn = miu.get_single_int(unit)
data.infall_warn_present = np.empty(dim_line, dtype="int") # Change name of variable???
data.infall_warn_age = np.empty(dim_line, dtype="float")
for i_row in range(data.dim_infall_warn):
row = unit.readline().split()
data.infall_warn_present[i_row] = int(row[0])
data.infall_warn_age[i_row] = float(row[1])
pass
#! end for
data.outflow_warn_present = miu.get_single_boolean(unit)
if data.outflow_warn_present:
data.outflow_warn_age = miu.get_single_float(unit)
pass
#! end if
data.output_age = np.empty(dim_output_age, dtype="float")
data.convol_time = np.empty(dim_output_age, dtype="float")
data.cosmic_time = np.empty(dim_output_age, dtype="float")
data.redshift = np.empty(dim_output_age, dtype="float")
data.galaxy_mass = np.empty(dim_output_age, dtype="float")
data.live_stars_mass = np.empty(dim_output_age, dtype="float")
data.WD_mass = np.empty(dim_output_age, dtype="float")
data.BHNS_mass = np.empty(dim_output_age, dtype="float")
data.inert_mass = np.empty(dim_output_age, dtype="float")
data.ISM_mass = np.empty(dim_output_age, dtype="float")
data.ISM_Z = np.empty(dim_output_age, dtype="float")
data.stel_Z_mass_avrg = np.empty(dim_output_age, dtype="float")
data.stel_Z_bol_avrg = np.empty(dim_output_age, dtype="float")
data.carb_abund = np.empty(dim_output_age, dtype="float")
data.sil_abund = np.empty(dim_output_age, dtype="float")
data.ISM_abund = np.empty((dim_output_age, data.dim_elem), dtype="float")
data.L_bol = np.empty(dim_output_age, dtype="float")
data.tau_V = np.empty(dim_output_age, dtype="float")
data.L_dust = np.empty(dim_output_age, dtype="float")
data.SF_rate = np.empty(dim_output_age, dtype="float")
data.Lyman_cont_rate = np.empty(dim_output_age, dtype="float")
data.CCSN_rate = np.empty(dim_output_age, dtype="float")
data.SNIa_rate = np.empty(dim_output_age, dtype="float")
data.stel_age_mass_avrg = np.empty(dim_output_age, dtype="float")
data.stel_age_bol_avrg = np.empty(dim_output_age, dtype="float")
data.Lyman_cont_gas_abs = np.empty(dim_output_age, dtype="float")
data.Lyman_cont_dust_abs = np.empty(dim_output_age, dtype="float")
data.ejec_rate_tot = np.empty(dim_output_age, dtype="float")
data.infall_rate = np.empty(dim_output_age, dtype="float")
data.outflow_rate = np.empty(dim_output_age, dtype="float")
data.ejec_cumul_mass = np.empty(dim_output_age, dtype="float")
data.SF_live_cumul_mass = np.empty(dim_output_age, dtype="float")
data.infall_cumul_mass = np.empty(dim_output_age, dtype="float")
data.outflow_cumul_mass = np.empty(dim_output_age, dtype="float")
data.L_dust_SFC = np.empty(dim_output_age, dtype="float")
data.L_dust_DISM = np.empty(dim_output_age, dtype="float")
data.opt_depth_warn_present = np.empty(dim_output_age, dtype="object")
data.opt_depth_warn_min_lambda = np.empty(dim_output_age, dtype="float")
data.opt_depth_warn_max_lambda = np.empty(dim_output_age, dtype="float")
data.lum_cont = np.empty((dim_output_age, dim_cont), dtype="float")
data.L_line = np.empty((dim_output_age, dim_line), dtype="float")
data.lum_stel_SFC_unatt \
= np.empty((dim_output_age, dim_cont), dtype="float")
data.lum_stel_DISM_unatt \
= np.empty((dim_output_age, dim_cont), dtype="float")
data.lum_neb_cont_SFC_unatt \
= np.empty((dim_output_age, dim_cont), dtype="float")
data.lum_neb_cont_DISM_unatt \
= np.empty((dim_output_age, dim_cont), dtype="float")
data.RF_cont_SFC = np.empty((dim_output_age, dim_cont), dtype="float")
data.RF_cont_DISM = np.empty((dim_output_age, dim_cont), dtype="float")
data.lum_species_SFC \
= np.empty((dim_species_SFC, dim_output_age, dim_cont), dtype="float")
data.lum_species_DISM \
= np.empty((dim_species_SFC, dim_output_age, dim_cont), dtype="float")
data.sublim_lum_species_SFC \
= np.empty((dim_species_SFC, dim_output_age, dim_cont), dtype="float")
data.sublim_lum_species_DISM \
= np.empty((dim_species_SFC, dim_output_age, dim_cont), dtype="float")
data.L_line_SFC_unatt = np.empty((dim_output_age, dim_line), dtype="float")
data.L_line_DISM_unatt = np.empty((dim_output_age, dim_line), dtype="float")
data.RF_line_SFC = np.empty((dim_output_age, dim_line), dtype="float")
data.RF_line_DISM = np.empty((dim_output_age, dim_line), dtype="float")
for i_time in range(dim_output_age):
data.output_age[i_time] = miu.get_single_float(unit)
data.convol_time[i_time] = miu.get_single_float(unit)
data.cosmic_time[i_time] = miu.get_single_float(unit)
data.redshift[i_time] = miu.get_single_float(unit)
data.galaxy_mass[i_time] = miu.get_single_float(unit)
data.live_stars_mass[i_time] = miu.get_single_float(unit)
data.WD_mass[i_time] = miu.get_single_float(unit)
data.BHNS_mass[i_time] = miu.get_single_float(unit)
data.inert_mass[i_time] = miu.get_single_float(unit)
data.ISM_mass[i_time] = miu.get_single_float(unit)
data.ISM_Z[i_time] = miu.get_single_float(unit)
data.stel_Z_mass_avrg[i_time] = miu.get_single_float(unit)
data.stel_Z_bol_avrg[i_time] = miu.get_single_float(unit)
data.carb_abund[i_time] = miu.get_single_float(unit)
data.sil_abund[i_time] = miu.get_single_float(unit)
row = unit.readline().split()
for i_col in range(data.dim_elem):
data.ISM_abund[i_time, i_col] = float(row[i_col])
pass
#! end for
data.L_bol[i_time] = miu.get_single_float(unit)
data.tau_V[i_time] = miu.get_single_float(unit)
data.L_dust[i_time] = miu.get_single_float(unit)
data.SF_rate[i_time] = miu.get_single_float(unit)
data.Lyman_cont_rate[i_time] = miu.get_single_float(unit)
data.CCSN_rate[i_time] = miu.get_single_float(unit)
data.SNIa_rate[i_time] = miu.get_single_float(unit)
data.stel_age_mass_avrg[i_time] = miu.get_single_float(unit)
data.stel_age_bol_avrg[i_time] = miu.get_single_float(unit)
data.Lyman_cont_gas_abs[i_time] = miu.get_single_float(unit)
data.Lyman_cont_dust_abs[i_time] = miu.get_single_float(unit)
data.ejec_rate_tot[i_time] = miu.get_single_float(unit)
data.infall_rate[i_time] = miu.get_single_float(unit)
data.outflow_rate[i_time] = miu.get_single_float(unit)
data.ejec_cumul_mass[i_time] = miu.get_single_float(unit)
data.SF_live_cumul_mass[i_time] = miu.get_single_float(unit)
data.infall_cumul_mass[i_time] = miu.get_single_float(unit)
data.outflow_cumul_mass[i_time] = miu.get_single_float(unit)
data.L_dust_SFC[i_time] = miu.get_single_float(unit)
data.L_dust_DISM[i_time] = miu.get_single_float(unit)
data.opt_depth_warn_present[i_time] \
= miu.get_single_boolean(unit)
if data.opt_depth_warn_present[i_time]:
data.opt_depth_warn_min_lambda[i_time] = miu.get_single_float(unit)
data.opt_depth_warn_max_lambda[i_time] = miu.get_single_float(unit)
pass
#! end if
if data.spectra_output == "detailed":
miu.skip_row(unit)
for i_cont in range(dim_cont):
if data.RF_output:
row = unit.readline().split()
data.lum_cont[i_time, i_cont] = float(row[0])
data.lum_stel_SFC_unatt[i_time, i_cont] = float(row[1])
data.lum_stel_DISM_unatt[i_time, i_cont] = float(row[2])
data.lum_neb_cont_SFC_unatt[i_time, i_cont] = float(row[3])
data.lum_neb_cont_DISM_unatt[i_time, i_cont] = float(row[4])
data.RF_cont_SFC[i_time, i_cont] = float(row[5])
data.RF_cont_DISM[i_time, i_cont] = float(row[6])
else:
row = unit.readline().split()
data.lum_cont[i_time, i_cont] = float(row[0])
data.lum_stel_SFC_unatt[i_time, i_cont] = float(row[1])
data.lum_stel_DISM_unatt[i_time, i_cont] = float(row[2])
data.lum_neb_cont_SFC_unatt[i_time, i_cont] = float(row[3])
data.lum_neb_cont_DISM_unatt[i_time, i_cont] = float(row[4])
pass
#! end if
pass
#! end for
miu.skip_row(unit)
for i_cont in range(dim_cont):
row = unit.readline().split()
for i_col in range(dim_species_SFC):
data.lum_species_SFC[i_col, i_time, i_cont] \
= float(row[i_col])
pass
#! end for
for i_col in range(dim_species_DISM):
data.lum_species_DISM[i_col, i_time, i_cont] \
= float(row[dim_species_SFC+i_col])
pass
#! end for
pass
#! end for
if data.sublim_output:
miu.skip_row(unit)
for i_cont in range(dim_cont):
row = unit.readline().split()
for i_col in range(dim_species_SFC):
data.sublim_lum_species_SFC[i_col, i_time, i_cont] \
= float(row[i_col])
pass
#! end for
for i_col in range(dim_species_DISM):
data.sublim_lum_species_DISM[i_col, i_time, i_cont] \
= float(row[dim_species_SFC+i_col])
pass
#! end for
pass
#! end for
pass
#! end if
miu.skip_row(unit)
for i_line in range(dim_line):
if data.RF_output:
row = unit.readline().split()
data.L_line[i_time, i_line] = float(row[0])
data.L_line_SFC_unatt[i_time, i_line] = float(row[1])
data.L_line_DISM_unatt[i_time, i_line] = float(row[2])
data.RF_line_SFC[i_time, i_line] = float(row[3])
data.RF_line_DISM[i_time, i_line] = float(row[4])
else:
row = unit.readline().split()
data.L_line[i_time, i_line] = float(row[0])
data.L_line_SFC_unatt[i_time, i_line] = float(row[1])
data.L_line_DISM_unatt[i_time, i_line] = float(row[2])
pass
#! end if
pass
#! end for
elif data.spectra_output == "basic":
miu.skip_row(unit)
i_cont = 0
while True:
row = unit.readline().split()
for i_col in range(len(row)):
data.lum_cont[i_time, i_cont] = float(row[i_col])
i_cont = i_cont+1
pass
#! end for
if i_cont == dim_cont: break
pass
#! end for
miu.skip_row(unit)
i_line = 0
while True:
row = unit.readline().split()
for i_col in range(len(row)):
data.L_line[i_time, i_line] = float(row[i_col])
i_line = i_line+1
pass
#! end for
if i_line == dim_line: break
pass
#! end while
else:
print("Wrong value for `spectra_output`. Stopped.")
sys.exit()
pass
#! end if
pass
#! end for
unit.close()
return data
def read_grain_SED(file_name):
#! Beware: Python lists start at index 0 whereas, by default,
#! Fortran arrays start at index 1.
#! Field named "lambda" in Fortran renamed "lambda_gr" in Python
#! because Python's `lambda` is used to create anonymous functions
#! (see Alonzo Church's "lambda calculus").
data = struct_grain_SED()
unit = open(file_name)
data.version_id = miu.get_single_string(unit)
data.version_date = miu.get_single_string(unit)
row = miu.next_non_comment_line(unit).split()
dim_age_grains = int(row[0])
dim_region = int(row[1])
data.dim_age_grains = dim_age_grains
data.dim_region = dim_region
data.age_grains = np.empty(dim_age_grains, dtype="float")
data.region = np.empty(dim_region, dtype="object")
for i_region in range(dim_region):
data.region[i_region] = struct_grain_SED1()
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
data.region[i_region].id = miu.clean_string(row[0])
row = row[1].split()
dim_species = int(row[0])
data.region[i_region].stoch_heating = miu.boolean(row[1])
data.region[i_region].dim_species = dim_species
data.region[i_region].species = np.empty(dim_species, dtype="object")
for i_species in range(dim_species):
data.region[i_region].species[i_species] = struct_grain_SED2()
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
data.region[i_region].species[i_species].id = miu.clean_string(row[0])
dim_lambda = int(row[1])
data.region[i_region].species[i_species].dim_lambda = dim_lambda
data.region[i_region].species[i_species].lambda_gr \
= np.empty(dim_lambda, dtype="float")
i_lambda = 0
while True:
row = unit.readline().split()
for i_col in range(len(row)):
data.region[i_region].species[i_species] \
.lambda_gr[i_lambda] = float(row[i_col])
i_lambda = i_lambda+1
pass
#! end for
if i_lambda == dim_lambda: break
pass
#! end while
pass
#! end for
pass
#! end for
for i_age in range(dim_age_grains):
for i_region in range(dim_region):
data.age_grains[i_age] = miu.get_single_float(unit)
eff_dim_species = miu.get_single_int(unit)
if eff_dim_species > 0:
for i_species in range(data.region[i_region].dim_species):
dim_radius = miu.get_single_int(unit)
data.region[i_region].species[i_species].dim_radius \
= dim_radius
if i_age == 0:
data.region[i_region].species[i_species] \
.radius = np.empty(dim_radius, dtype="float")
dim_lambda = data.region[i_region].species[i_species] \
.dim_lambda
if data.region[i_region].stoch_heating:
data.region[i_region].species[i_species].lum_stoch \
= np.empty((dim_age_grains, dim_radius, \
dim_lambda), dtype="float")
pass
#! end if
data.region[i_region].species[i_species].lum_eq \
= np.empty((dim_age_grains, dim_radius, \
dim_lambda), dtype="float")
pass
#! end if
for i_radius in range(dim_radius):
data.region[i_region].species[i_species] \
.radius[i_radius] = miu.get_single_float(unit)
if data.region[i_region].stoch_heating:
i_lambda = 0
while True:
row = unit.readline().split()
for i_col in range(len(row)):
data.region[i_region].species[i_species] \
.lum_stoch[i_age, i_radius, i_lambda] \
= float(row[i_col])
i_lambda = i_lambda+1
pass
#! end for
if i_lambda == dim_lambda: break
pass
#! end while
pass
#! end if
i_lambda = 0
while True:
row = unit.readline().split()
for i_col in range(len(row)):
data.region[i_region].species[i_species] \
.lum_eq[i_age, i_radius, i_lambda] \
= float(row[i_col])
i_lambda = i_lambda+1
pass
#! end for
if i_lambda == dim_lambda: break
pass
#! end while
pass
#! end do
pass
#! end do
else:
pass
#! end if
pass
#! end do
pass
#! end do
unit.close()
return data
def read_grain_temp(file_name):
#! Beware: Python lists start at index 0 whereas, by default,
#! Fortran arrays start at index 1.
data = struct_grain_temp()
unit = open(file_name)
data.version_id = miu.get_single_string(unit)
data.version_date = miu.get_single_string(unit)
row = miu.next_non_comment_line(unit).split()
dim_age_grains = int(row[0])
dim_region = int(row[1])
data.dim_age_grains = dim_age_grains
data.dim_region = dim_region
data.age_grains = np.empty(dim_age_grains, dtype="float")
data.region = np.empty(dim_region, dtype="object")
for i_region in range(dim_region):
data.region[i_region] = struct_grain_temp1()
row = unit.readline().strip().replace('" ', '" , ').split(' , ')
data.region[i_region].id = miu.clean_string(row[0])
row = row[1].split()
dim_species = int(row[0])
data.region[i_region].stoch_heating = miu.boolean(row[1])
data.region[i_region].dim_species = dim_species
data.region[i_region].species = np.empty(dim_species, dtype="object")
for i_species in range(dim_species):
data.region[i_region].species[i_species] = struct_grain_temp2()
data.region[i_region].species[i_species].id \
= miu.get_single_string(unit)
pass
#! end for
pass
#! end for
for i_age in range(dim_age_grains):
for i_region in range(dim_region):
data.age_grains[i_age] = miu.get_single_float(unit)
eff_dim_species = miu.get_single_int(unit)
if eff_dim_species > 0:
for i_species in range(data.region[i_region].dim_species):
dim_radius = miu.get_single_int(unit)
data.region[i_region].species[i_species].dim_radius \
= dim_radius
if i_age == 0:
data.region[i_region].species[i_species] \
.radius = np.empty(dim_radius, dtype="float")
data.region[i_region].species[i_species] \
.state = np.empty((dim_age_grains, dim_radius), \
dtype="object")
pass
#! end if
for i_radius in range(dim_radius):
if data.region[i_region].stoch_heating:
row = unit.readline().split()
data.region[i_region].species[i_species] \
.radius[i_radius] = float(row[0])
data.region[i_region].species[i_species] \
.state[i_age, i_radius] = struct_grain_temp3()
data.region[i_region].species[i_species] \
.state[i_age, i_radius].temp_eq = float(row[1])
dim_temp = int(row[2])
data.region[i_region].species[i_species] \
.state[i_age, i_radius].dim_temp = dim_temp
data.region[i_region].species[i_species] \
.state[i_age, i_radius].temp \
= np.empty(dim_temp, dtype="float")
data.region[i_region].species[i_species] \
.state[i_age, i_radius].prob \
= np.empty(dim_temp, dtype="float")
for i_temp in range(dim_temp):
row = unit.readline().split()
data.region[i_region].species[i_species] \
.state[i_age, i_radius].temp[i_temp] \
= float(row[0])
data.region[i_region].species[i_species] \
.state[i_age, i_radius].prob[i_temp] \
= float(row[1])
pass
#! end for
pass
#!
else:
row = unit.readline().split()
data.region[i_region].species[i_species] \
.radius[i_radius] = float(row[0])
data.region[i_region].species[i_species] \
.state[i_age, i_radius] = struct_grain_temp3()
data.region[i_region].species[i_species] \
.state[i_age, i_radius].temp_eq = float(row[1])
pass
#! end if
pass
#! end do
pass
#! end do
else:
pass
#! end if
pass
#! end do
pass
#! end do
unit.close()
return data