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Commit 7f8fa34f authored by David Hendriks's avatar David Hendriks
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binarycpython/utils/custom_logging_functions.py
+ 2
− 4
View file @ 7f8fa34f
......@@ -213,10 +213,8 @@ def return_compilation_dict(verbose=0):
libs = defaults[
"libs"
] # = ($ENV{BINARY_GRID2_LIBS} // $defaults{libs}).' '.($ENV{BINARY_GRID2_EXTRALIBS}//'');
ccflags = defaults[
"ccflags"
] # = $ENV{BINARY_GRID2_CCFLAGS}
# // ($defaults{ccflags}) . ($ENV{BINARY_GRID2_EXTRACCFLAGS} // '');
ccflags = defaults["ccflags"] # = $ENV{BINARY_GRID2_CCFLAGS}
# // ($defaults{ccflags}) . ($ENV{BINARY_GRID2_EXTRACCFLAGS} // '');
# you must define _SEARCH_H to prevent it being loaded twice
ccflags += " -shared -D_SEARCH_H"
......
binarycpython/utils/distribution_functions.py
+ 44
− 28
View file @ 7f8fa34f
......@@ -9,16 +9,14 @@ generate probability distributions for sampling populations
import math
import numpy as np
from binarycpython.utils.useful_funcs import (
calc_period_from_sep,
)
from binarycpython.utils.useful_funcs import calc_period_from_sep
###
# File containing probability distributions
# Mostly copied from the perl modules
# TODO: make some things globally present? rob does this in his module.
# i guess it saves calculations but not sure if im gonna do that now
# TODO: make some things globally present? rob does this in his module.
# i guess it saves calculations but not sure if im gonna do that now
# TODO: Add the stuff from the IMF file
# TODO: call all of these functions to check whether they work
# TODO: make global constants stuff
......@@ -26,7 +24,9 @@ from binarycpython.utils.useful_funcs import (
LOG_LN_CONVERTER = 1.0 / math.log(10.0)
distribution_constants = {} # To store the constants in
distribution_constants = {} # To store the constants in
def prepare_dict(global_dict, list_of_sub_keys):
"""
Function that makes sure that the global dict is prepared to have a value set there
......@@ -44,7 +44,6 @@ def prepare_dict(global_dict, list_of_sub_keys):
internal_dict_value = internal_dict_value[k]
def flat():
"""
Dummy distribution function that returns 1
......@@ -467,7 +466,7 @@ def duquennoy1991(logper):
return gaussian(logper, 4.8, 2.3, -2, 12)
def sana12(M1, M2, a, P, amin, amax, x0, x1, p):
def sana12(M1, M2, a, P, amin, amax, x0, x1, p):
"""
distribution of initial orbital periods as found by Sana et al. (2012)
which is a flat distribution in ln(a) and ln(P) respectively for stars
......@@ -517,6 +516,7 @@ def sana12(M1, M2, a, P, amin, amax, x0, x1, p):
return res
def Izzard2012_period_distribution(P, M1, log10Pmin=1):
"""
period distribution which interpolates between
......@@ -544,42 +544,56 @@ def Izzard2012_period_distribution(P, M1, log10Pmin=1):
print("Izzard2012 called for M={} (trunc'd to {}), P={}\n".format(Mwas, M1, P))
# Calculate the normalisations
# need to normalize the distribution for this mass
# need to normalize the distribution for this mass
# (and perhaps secondary mass)
prepare_dict(distribution_constants, ['Izzard2012', M1])
if not distribution_constants['Izzard2012'][M1].get(log10Pmin):
distribution_constants['Izzard2012'][M1][log10Pmin] = 1 # To prevent this loop from going recursive
N = 200.0 # Resolution for normalisation. I hope 1000 is enough
dlP = (10.0 - log10Pmin)/N
C = 0 # normalisation const.
prepare_dict(distribution_constants, ["Izzard2012", M1])
if not distribution_constants["Izzard2012"][M1].get(log10Pmin):
distribution_constants["Izzard2012"][M1][
log10Pmin
] = 1 # To prevent this loop from going recursive
N = 200.0 # Resolution for normalisation. I hope 1000 is enough
dlP = (10.0 - log10Pmin) / N
C = 0 # normalisation const.
for lP in np.arange(log10Pmin, 10, dlP):
C += dlP * Izzard2012_period_distribution(10**lP, M1, log10Pmin)
C += dlP * Izzard2012_period_distribution(10 ** lP, M1, log10Pmin)
distribution_constants['Izzard2012'][M1][log10Pmin] = 1.0/C;
print("Normalization constant for Izzard2012 M={} (log10Pmin={}) is\
{}\n".format(M1, log10Pmin, distribution_constants['Izzard2012'][M1][log10Pmin]))
distribution_constants["Izzard2012"][M1][log10Pmin] = 1.0 / C
print(
"Normalization constant for Izzard2012 M={} (log10Pmin={}) is\
{}\n".format(
M1, log10Pmin, distribution_constants["Izzard2012"][M1][log10Pmin]
)
)
lP = math.log10(P); # log period
lP = math.log10(P)
# log period
# # fits
mu = interpolate_in_mass_izzard2012(M1, -17.8, 5.03)
sigma = interpolate_in_mass_izzard2012(M1, 9.18, 2.28)
K = interpolate_in_mass_izzard2012(M1, 6.93e-2, 0.0)
nu = interpolate_in_mass_izzard2012(M1, 0.3, -1)
g = 1.0/(1.0+1e-30**(lP-nu))
g = 1.0 / (1.0 + 1e-30 ** (lP - nu))
lPmu = lP - mu
print("M={} ({}) P={} : mu={} sigma={} K={} nu={} norm=%g\n".format(
Mwas, M1, P, mu, sigma, K, nu))
print(
"M={} ({}) P={} : mu={} sigma={} K={} nu={} norm=%g\n".format(
Mwas, M1, P, mu, sigma, K, nu
)
)
# print "FUNC $distdata{Izzard2012}{$M}{$log10Pmin} * (exp(- (x-$mu)**2/(2.0*$sigma*$sigma) ) + $K/MAX(0.1,$lP)) * $g;\n";
if ((lP < log10Pmin) or (lP > 10.0)):
if (lP < log10Pmin) or (lP > 10.0):
return 0
else:
return distribution_constants['Izzard2012'][M1][log10Pmin] * (math.exp(- lPmu * lPmu / (2.0 * sigma * sigma)) + K/max(0.1, lP)) * g;
return (
distribution_constants["Izzard2012"][M1][log10Pmin]
* (math.exp(-lPmu * lPmu / (2.0 * sigma * sigma)) + K / max(0.1, lP))
* g
)
def interpolate_in_mass_izzard2012(M, high, low):
"""
......@@ -592,9 +606,11 @@ def interpolate_in_mass_izzard2012(M, high, low):
log_interpolation = False
if log_interpolation:
return (high-low)/(math.log10(16.3)-math.log10(1.15)) * (math.log10(M)-math.log10(1.15)) + low
return (high - low) / (math.log10(16.3) - math.log10(1.15)) * (
math.log10(M) - math.log10(1.15)
) + low
else:
return (high-low)/(16.3-1.15) * (M-1.15) + low
return (high - low) / (16.3 - 1.15) * (M - 1.15) + low
# print(sana12(10, 2, 10, 100, 1, 1000, math.log(10), math.log(1000), 6))
......
binarycpython/utils/grid.py
+ 46
− 48
View file @ 7f8fa34f
......@@ -16,6 +16,7 @@ import argparse
import importlib.util
from pathos.helpers import mp as pathos_multiprocess
# from pathos.multiprocessing import ProcessingPool as Pool
from pathos.pools import _ProcessPool as Pool
......@@ -53,7 +54,7 @@ import binary_c_python_api
# that is stored into a python object. rather its just written to stdout
class Population():
class Population:
"""
Population Object. Contains all the necessary functions to set up, run and process a
population of systems
......@@ -68,10 +69,8 @@ class Population():
self.cleaned_up_defaults = self.cleanup_defaults()
# Different sections of options
self.bse_options = (
{}
) # bse_options is just empty.
# Setting stuff will check against the defaults to see if the input is correct.
self.bse_options = {} # bse_options is just empty.
# Setting stuff will check against the defaults to see if the input is correct.
self.grid_options = grid_options_defaults_dict.copy()
self.custom_options = {}
......@@ -136,7 +135,9 @@ class Population():
else:
print(
"!! Key doesnt match previously known parameter: \
adding: {}={} to custom_options".format(key, kwargs[key])
adding: {}={} to custom_options".format(
key, kwargs[key]
)
)
self.custom_options[key] = kwargs[key]
......@@ -206,17 +207,17 @@ class Population():
pass
def add_grid_variable(
self,
name,
longname,
valuerange,
resolution,
spacingfunc,
probdist,
dphasevol,
parameter_name,
precode=None,
condition=None,
self,
name,
longname,
valuerange,
resolution,
spacingfunc,
probdist,
dphasevol,
parameter_name,
precode=None,
condition=None,
):
"""spec
Function to add grid variables to the grid_options.
......@@ -316,11 +317,11 @@ class Population():
return self.defaults
def return_all_info(
self,
include_population_settings=True,
include_binary_c_defaults=True,
include_binary_c_version_info=True,
include_binary_c_help_all=True,
self,
include_population_settings=True,
include_binary_c_defaults=True,
include_binary_c_version_info=True,
include_binary_c_help_all=True,
):
"""
Function that returns all the information about the population and binary_c
......@@ -350,13 +351,13 @@ class Population():
return all_info
def export_all_info(
self,
use_datadir=True,
outfile=None,
include_population_settings=True,
include_binary_c_defaults=True,
include_binary_c_version_info=True,
include_binary_c_help_all=True,
self,
use_datadir=True,
outfile=None,
include_population_settings=True,
include_binary_c_defaults=True,
include_binary_c_version_info=True,
include_binary_c_help_all=True,
):
"""
Function that exports the all_info to a json file
......@@ -383,7 +384,7 @@ class Population():
# Clean the all_info_dict: (i.e. transform the function objects to strings)
if all_info_cleaned.get("population_settings", None):
if all_info_cleaned["population_settings"]["grid_options"][
"parse_function"
"parse_function"
]:
all_info_cleaned["population_settings"]["grid_options"][
"parse_function"
......@@ -680,7 +681,6 @@ class Population():
if self.grid_options["parse_function"]:
self.grid_options["parse_function"](self, out)
def evolve_population(self):
"""
Function to evolve populations. This is the main function. Handles the setting up, evolving
......@@ -696,8 +696,8 @@ class Population():
# Evolve systems: via grid_options one can choose to do this linearly, or
# multiprocessing method.
if (
self.grid_options["evolution_type"]
in self.grid_options["evolution_type_options"]
self.grid_options["evolution_type"]
in self.grid_options["evolution_type_options"]
):
if self.grid_options["evolution_type"] == "mp":
self.evolve_population_mp()
......@@ -837,8 +837,8 @@ class Population():
code_string += indent * depth + "# setting probability lists\n"
# Prepare the probability
for grid_variable_el in sorted(
self.grid_options["grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
self.grid_options["grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
):
# Make probabilities dict
grid_variable = grid_variable_el[1]
......@@ -853,10 +853,10 @@ class Population():
# Generate code
print("Generating grid code")
for loopnr, grid_variable_el in enumerate(
sorted(
self.grid_options["grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
)
sorted(
self.grid_options["grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
)
):
print("Constructing/adding: {}".format(grid_variable_el[0]))
grid_variable = grid_variable_el[1]
......@@ -1099,11 +1099,11 @@ class Population():
# this has to go in a reverse order:
# Here comes the stuff that is put after the deepest nested part that calls returns stuff.
for loopnr, grid_variable_el in enumerate(
sorted(
self.grid_options["grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
reverse=True,
)
sorted(
self.grid_options["grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
reverse=True,
)
):
grid_variable = grid_variable_el[1]
code_string += indent * (depth + 1) + "#" * 40 + "\n"
......@@ -1252,10 +1252,7 @@ class Population():
###################################################
def write_binary_c_calls_to_file(
self,
output_dir=None,
output_filename=None,
include_defaults=False
self, output_dir=None, output_filename=None, include_defaults=False
):
"""
Function that loops over the gridcode and writes the generated parameters to a file.
......@@ -1389,4 +1386,5 @@ class Population():
# Function to join the result dictionaries
# """
################################################################################################
binarycpython/utils/grid_options_defaults.py
+ 7
− 7
View file @ 7f8fa34f
......@@ -34,8 +34,8 @@ grid_options_defaults_dict = {
# Custom logging
##########################
"C_auto_logging": None, # Should contain a dictionary where the kes are they headers
# and the values are lists of parameters that should be logged.
# This will get parsed by autogen_C_logging_code in custom_loggion_functions.py
# and the values are lists of parameters that should be logged.
# This will get parsed by autogen_C_logging_code in custom_loggion_functions.py
"C_logging_code": None, # Should contain a string which holds the logging code.
"custom_logging_func_memaddr": -1, # Contains the custom_logging functions memory address
"custom_logging_shared_library_file": None,
......@@ -43,7 +43,7 @@ grid_options_defaults_dict = {
# Store pre-loading:
##########################
"store_memaddr": -1, # Contains the store object memory adress, useful for preloading.
# defaults to -1 and isnt used if thats the default then.
# defaults to -1 and isnt used if thats the default then.
##########################
# Log args: logging of arguments
##########################
......@@ -59,7 +59,7 @@ grid_options_defaults_dict = {
"linear",
], # available choices for type of population evolution
"system_generator": None, # value that holds the function that generates the system
# (result of building the grid script)
# (result of building the grid script)
"population_type": "grid", #
"population_type_options": [
"grid",
......@@ -144,7 +144,7 @@ grid_options_defaults_dict = {
# condor_postpone_join=>0, # if 1, data is not joined, e.g. if you
# # want to do it off the condor grid (e.g. with more RAM)
# condor_join_machine=>undef, # if defined then this is the machine on which the join command
#should be launched (must be sshable and not postponed)
# should be launched (must be sshable and not postponed)
# condor_join_pwd=>undef, # directory the join should be in
# # (defaults to $ENV{PWD} if undef)
# condor_memory=>1024, # in MB, the memory use (ImageSize) of the job
......@@ -153,10 +153,10 @@ grid_options_defaults_dict = {
# condor_load_from_snapshot=>0, # if 1 check for snapshot .sv file and load it if found
# condor_checkpoint_interval=>0, # checkpoint interval (seconds)
# condor_checkpoint_stamp_times=>0, # if 1 then files are given timestamped names
# (warning: lots of files!), otherwise just store the lates
# (warning: lots of files!), otherwise just store the lates
# condor_streams=>0, # stream stderr/stdout by default (warning: might cause heavy network load)
# condor_save_joined_file=>0, # if 1 then results/joined contains the results
# (useful for debugging, otherwise a lot of work)
# (useful for debugging, otherwise a lot of work)
# condor_requirements=>'', # used?
# # resubmit options : if the status of a condor script is
# # either 'finished','submitted','running' or 'crashed',
......
binarycpython/utils/plot_functions.py
+ 2
− 3
View file @ 7f8fa34f
......@@ -21,9 +21,7 @@ import numpy as np
# import matplotlib.pyplot as plt
from binarycpython.utils.functions import (
output_lines,
)
from binarycpython.utils.functions import output_lines
from binarycpython.utils.run_system_wrapper import run_system
from binarycpython.utils.custom_logging_functions import binary_c_log_code
......@@ -67,6 +65,7 @@ def dummy():
"""Placeholder"""
pass
def parse_function_hr_diagram(output):
"""
Parsing function for the HR plotting routine
......
docs/source/conf.py
+ 4
− 4
View file @ 7f8fa34f
......@@ -40,10 +40,10 @@ author = "David Hendriks, Robert Izzard, Jeff Andrews"
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.doctest",
'sphinx.ext.todo',
'sphinx.ext.coverage',
'sphinx.ext.viewcode',
'sphinx.ext.napoleon',
"sphinx.ext.todo",
"sphinx.ext.coverage",
"sphinx.ext.viewcode",
"sphinx.ext.napoleon",
"hawkmoth",
"m2r",
]
......
examples/example_plotting_distributions.py
+ 61
− 42
View file @ 7f8fa34f
......@@ -8,14 +8,12 @@ from binarycpython.utils.distribution_functions import (
Kroupa2001,
Arenou2010_binary_fraction,
raghavan2010_binary_fraction,
imf_scalo1998,
imf_scalo1986,
imf_tinsley1980,
imf_scalo1998,
imf_chabrier2003,
flatsections,
duquennoy1991,
sana12,
Izzard2012_period_distribution,
......@@ -30,8 +28,9 @@ from binarycpython.utils.useful_funcs import calc_sep_from_period
# mass distribution plots
################################################
def plot_mass_distributions():
mass_values = np.arange(0.11, 80, .1)
mass_values = np.arange(0.11, 80, 0.1)
kroupa_probability = [Kroupa2001(mass) for mass in mass_values]
scalo1986 = [imf_scalo1986(mass) for mass in mass_values]
......@@ -39,64 +38,75 @@ def plot_mass_distributions():
scalo1998 = [imf_scalo1998(mass) for mass in mass_values]
chabrier2003 = [imf_chabrier2003(mass) for mass in mass_values]
plt.plot(mass_values, kroupa_probability, label='Kroupa')
plt.plot(mass_values, scalo1986, label='scalo1986')
plt.plot(mass_values, tinsley1980, label='tinsley1980')
plt.plot(mass_values, scalo1998, label='scalo1998')
plt.plot(mass_values, chabrier2003, label='chabrier2003')
plt.title('Probability distribution for mass of primary')
plt.ylabel(r'Probability')
plt.xlabel(r'Mass (M$_{\odot}$)')
plt.yscale('log')
plt.xscale('log')
plt.plot(mass_values, kroupa_probability, label="Kroupa")
plt.plot(mass_values, scalo1986, label="scalo1986")
plt.plot(mass_values, tinsley1980, label="tinsley1980")
plt.plot(mass_values, scalo1998, label="scalo1998")
plt.plot(mass_values, chabrier2003, label="chabrier2003")
plt.title("Probability distribution for mass of primary")
plt.ylabel(r"Probability")
plt.xlabel(r"Mass (M$_{\odot}$)")
plt.yscale("log")
plt.xscale("log")
plt.grid()
plt.legend()
plt.show()
################################################
# Binary fraction distributions
################################################
def plot_binary_fraction_distributions():
arenou_binary_distibution = [Arenou2010_binary_fraction(mass) for mass in mass_values]
raghavan2010_binary_distribution = [raghavan2010_binary_fraction(mass) for mass in mass_values ]
plt.plot(mass_values, arenou_binary_distibution, label='arenou 2010')
plt.plot(mass_values, raghavan2010_binary_distribution, label='Raghavan 2010')
plt.title('Binary fractions distributions')
plt.ylabel(r'Binary fraction')
plt.xlabel(r'Mass (M$_{\odot}$)')
arenou_binary_distibution = [
Arenou2010_binary_fraction(mass) for mass in mass_values
]
raghavan2010_binary_distribution = [
raghavan2010_binary_fraction(mass) for mass in mass_values
]
plt.plot(mass_values, arenou_binary_distibution, label="arenou 2010")
plt.plot(mass_values, raghavan2010_binary_distribution, label="Raghavan 2010")
plt.title("Binary fractions distributions")
plt.ylabel(r"Binary fraction")
plt.xlabel(r"Mass (M$_{\odot}$)")
# plt.yscale('log')
plt.xscale('log')
plt.xscale("log")
plt.grid()
plt.legend()
plt.show()
################################################
# Mass ratio distributions
################################################
def plot_mass_ratio_distributions():
mass_ratios = np.arange(0, 1, .01)
mass_ratios = np.arange(0, 1, 0.01)
example_mass = 2
flat_dist = [
flatsections(
q,
opts=[
{'min':0.1/example_mass, 'max':0.8, 'height':1},
{'min': 0.8, 'max':1.0, 'height': 1.0}
]
) for q in mass_ratios]
plt.plot(mass_ratios, flat_dist, label='Flat')
plt.title('Mass ratio distributions')
plt.ylabel(r'Probability')
plt.xlabel(r'Mass ratio (q = $\frac{M1}{M2}$) ')
{"min": 0.1 / example_mass, "max": 0.8, "height": 1},
{"min": 0.8, "max": 1.0, "height": 1.0},
],
)
for q in mass_ratios
]
plt.plot(mass_ratios, flat_dist, label="Flat")
plt.title("Mass ratio distributions")
plt.ylabel(r"Probability")
plt.xlabel(r"Mass ratio (q = $\frac{M1}{M2}$) ")
plt.grid()
plt.legend()
plt.show()
################################################
# Period distributions
################################################
......@@ -161,7 +171,6 @@ def plot_period_distributions():
for logper in logperiod_values
]
m1 = 30
m2 = 30
sana12_distribution_q1 = [
......@@ -179,17 +188,29 @@ def plot_period_distributions():
for logper in logperiod_values
]
Izzard2012_period_distribution_10 = [Izzard2012_period_distribution(10**logperiod, 10) for logperiod in logperiod_values]
Izzard2012_period_distribution_20 = [Izzard2012_period_distribution(10**logperiod, 20) for logperiod in logperiod_values]
Izzard2012_period_distribution_10 = [
Izzard2012_period_distribution(10 ** logperiod, 10)
for logperiod in logperiod_values
]
Izzard2012_period_distribution_20 = [
Izzard2012_period_distribution(10 ** logperiod, 20)
for logperiod in logperiod_values
]
plt.plot(logperiod_values, duquennoy1991_distribution, label="Duquennoy & Mayor 1991")
plt.plot(
logperiod_values, duquennoy1991_distribution, label="Duquennoy & Mayor 1991"
)
plt.plot(logperiod_values, sana12_distribution_q0033, label="Sana 12 (q=0.033)")
plt.plot(logperiod_values, sana12_distribution_q05, label="Sana 12 (q=0.5)")
plt.plot(logperiod_values, sana12_distribution_q01, label="Sana 12 (q=0.1)")
plt.plot(logperiod_values, sana12_distribution_q1, label="Sana 12 (q=1)")
plt.plot(logperiod_values, Izzard2012_period_distribution_10, label='Izzard2012 (M=10)')
plt.plot(logperiod_values, Izzard2012_period_distribution_20, label='Izzard2012 (M=20)')
plt.plot(
logperiod_values, Izzard2012_period_distribution_10, label="Izzard2012 (M=10)"
)
plt.plot(
logperiod_values, Izzard2012_period_distribution_20, label="Izzard2012 (M=20)"
)
plt.title("Period distributions")
plt.ylabel(r"Probability")
......@@ -198,6 +219,7 @@ def plot_period_distributions():
plt.legend()
plt.show()
plot_period_distributions()
################################################
# Sampling part of distribution and calculating probability ratio
......@@ -206,9 +228,6 @@ plot_period_distributions()
# TODO show the difference between sampling over the full range, or taking a smaller range initially and compensating for it.
# val = Izzard2012_period_distribution(1000, 10)
# print(val)
# val2 = Izzard2012_period_distribution(100, 10)
......
setup.py
+ 10
− 4
View file @ 7f8fa34f
......@@ -28,12 +28,16 @@ CWD = os.getcwd()
BINARY_C_CONFIG = os.environ["BINARY_C"] + "/binary_c-config"
BINARY_C_INCDIRS = (
subprocess.run([BINARY_C_CONFIG, "incdirs_list"], stdout=subprocess.PIPE, check=True)
subprocess.run(
[BINARY_C_CONFIG, "incdirs_list"], stdout=subprocess.PIPE, check=True
)
.stdout.decode("utf-8")
.split()
)
BINARY_C_LIBDIRS = (
subprocess.run([BINARY_C_CONFIG, "libdirs_list"], stdout=subprocess.PIPE, check=True)
subprocess.run(
[BINARY_C_CONFIG, "libdirs_list"], stdout=subprocess.PIPE, check=True
)
.stdout.decode("utf-8")
.split()
)
......@@ -52,7 +56,9 @@ BINARY_C_LIBS = (
# create list of tuples of defined macros
BINARY_C_DEFINE_MACROS = []
DEFINES = (
subprocess.run([BINARY_C_CONFIG, "define_macros"], stdout=subprocess.PIPE, check=True)
subprocess.run(
[BINARY_C_CONFIG, "define_macros"], stdout=subprocess.PIPE, check=True
)
.stdout.decode("utf-8")
.split()
)
......@@ -123,7 +129,7 @@ BINARY_C_PYTHON_API_MODULE = Extension(
include_dirs=INCLUDE_DIRS,
libraries=LIBRARIES,
library_dirs=LIBRARY_DIRS,
runtime_library_dirs=RUNTIME_LIBRARY_DIRS,
runtime_library_dirs=RUNTIME_LIBRARY_DIRS,
define_macros=[] + BINARY_C_DEFINE_MACROS,
extra_objects=[],
extra_compile_args=[],
......
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