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import os
import json
import time
import pickle
import sys
import matplotlib.pyplot as plt
from binarycpython.utils.grid import Population
from binarycpython.utils.functions import get_help_all, get_help, create_hdf5, output_lines
###
# Script to generate BH MS systems.
def parse_function(self, output):
# extract info from the population instance
# TODO: think about whether this is smart. Passing around this object might be an overkill
####################################################
# Get some information from the grid
data_dir = self.custom_options['data_dir']
base_filename = self.custom_options['base_filename']
# Check directory, make if necessary
os.makedirs(data_dir, exist_ok=True)
####################################################
#
seperator = ' '
# Create filename
outfilename = os.path.join(data_dir, base_filename)
result_header = ['zams_mass', 'st_0', 'st_1', 'st_2', 'st_3', 'st_4', 'st_5', 'st_6', 'st_7', 'st_8', 'st_9', 'st_10', 'st_11', 'st_12', 'st_13', 'st_14', 'st_15']
mass_lost_dict = {}
for i in range(16):
mass_lost_dict['{}'.format(i)] = 0
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# Go over the output.
for el in output_lines(output):
headerline = el.split()[0]
# Check the header and act accordingly
if (headerline=='DAVID_MASSLOSS_SN'):
parameters = ['time', 'mass_1', 'prev_mass_1', 'zams_mass_1', 'stellar_type', 'probability']
values = el.split()[1:]
if not float(values[0])==0.0:
mass_lost = float(values[2])-float(values[1])
mass_lost_dict[values[4]] += mass_lost
initial_mass = values[3]
total_mass_lost += mass_lost
result_list = [initial_mass]
for key in mass_lost_dict.keys():
result_list.append(str(mass_lost_dict[key]))
result_dict = self.grid_options['result_dict']
# This trick is necessary
# Make the mass dict and set values
result_dict['mass'] = result_dict.get('mass', {})
mass_result = result_dict['mass']
mass_result[initial_mass] = mass_result.get(initial_mass, 0) + total_mass_lost
result_dict['mass'] = mass_result
result_dict['probability'] = result_dict.get('probability', 0) + 0.00002123
## Set values
test_pop = Population()
test_pop.set(
C_logging_code="""
Printf("DAVID_MASSLOSS_SN %30.12e %g %g %g %d %g\\n",
//
stardata->model.time, // 1
stardata->star[0].mass, //2
stardata->previous_stardata->star[0].mass, //3
stardata->star[0].pms_mass, //4
stardata->star[0].stellar_type, //5
stardata->model.probability //6
);
""")
# Set grid variables
resolution = {'M_1': 5, 'q': 5, 'per': 5}
test_pop.add_grid_variable(
name="lnm1",
longname="Primary mass",
valuerange=[1, 150],
resolution="{}".format(resolution["M_1"]),
spacingfunc="const(math.log(1), math.log(150), {})".format(resolution["M_1"]),
precode="M_1=math.exp(lnm1)",
probdist="three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1",
dphasevol="dlnm1",
parameter_name="M_1",
condition="", # Impose a condition on this grid variable. Mostly for a check for yourself
)
test_pop.add_grid_variable(
name="q",
longname="Mass ratio",
valuerange=["0.1/M_1", 1],
resolution="{}".format(resolution['q']),
spacingfunc="const(0.1/M_1, 1, {})".format(resolution['q']),
probdist="flatsections(q, [{'min': 0.1/M_1, 'max': 0.8, 'height': 1}, {'min': 0.8, 'max': 1.0, 'height': 1.0}])",
dphasevol="dq",
precode="M_2 = q * M_1",
parameter_name="M_2",
condition="", # Impose a condition on this grid variable. Mostly for a check for yourself
)
test_pop.add_grid_variable(
name="logper",
longname="log(Orbital_Period)",
valuerange=[-2, 12],
resolution="{}".format(resolution["per"]),
spacingfunc="np.linspace(-2, 12, {})".format(resolution["per"]),
precode="orbital_period = 10** logper\n", # TODO:
probdist="gaussian(logper,4.8, 2.3, -2.0, 12.0)",
parameter_name="orbital_period",
dphasevol="dln10per",
)
##########################################################################
metallicity = 0.002
test_pop.set(
separation=1000000000,
orbital_period=400000000,
metallicity=metallicity,
M_1=100,
M_2=5,
verbose=1,
data_dir=os.path.join(os.environ['BINARYC_DATA_ROOT'], 'testing_python', 'BHMS'),
base_filename="BH_MS_z{}.dat".format(metallicity),
parse_function=parse_function,
amt_cores=2,
)
# out = test_pop.evolve_single()
# print(out)
# quit()
test_pop.evolve_population()
def handle_output(test_pop):
# $results is a hash reference containing
# the results that were added up in parse_data()
results = test_pop.grid_options['result_dict']
print(results)
# output the mass distribution
handle_output(test_pop)
# print(test_pop.grid_options['results_per_worker'])
# # Export settings:
# test_pop.export_all_info(use_datadir=True)
# # hdf5
# create_hdf5(test_pop.custom_options['data_dir'], name="BH_MS_z{}.hdf5".format(metallicity))