diff --git a/.gitignore b/.gitignore
index 1657d18e9c8e19217926765eeedba6bc603e5ac9..25de861a1db6009fcd0c62652d0e745b4547d292 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
+snippets/
+binarycpython_snippets
core
notes/
-snippets/
make_output.txt
tests/population/scaling/scaling_plots/*
tests/population/scaling/scaling_results/*
diff --git a/coverage.svg b/badges/coverage.svg
similarity index 100%
rename from coverage.svg
rename to badges/coverage.svg
diff --git a/badges/docstring_coverage.svg b/badges/docstring_coverage.svg
new file mode 100644
index 0000000000000000000000000000000000000000..3bbc9abdff8b8656da7208d061e7a9227206711f
--- /dev/null
+++ b/badges/docstring_coverage.svg
@@ -0,0 +1,20 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="142" height="20">
+ <linearGradient id="s" x2="0" y2="100%">
+ <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
+ <stop offset="1" stop-opacity=".1"/>
+ </linearGradient>
+ <clipPath id="r">
+ <rect width="142" height="20" rx="3" fill="#fff"/>
+ </clipPath>
+ <g clip-path="url(#r)">
+ <rect width="99" height="20" fill="#555"/>
+ <rect x="99" width="43" height="20" fill="#dfb317"/>
+ <rect width="142" height="20" fill="url(#s)"/>
+ </g>
+ <g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" font-size="110">
+ <text x="505" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="890">docstr-coverage</text>
+ <text x="505" y="140" transform="scale(.1)" textLength="890">docstr-coverage</text>
+ <text x="1195" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)">74%</text>
+ <text x="1195" y="140" transform="scale(.1)">74%</text>
+ </g>
+</svg>
\ No newline at end of file
diff --git a/interrogate_badge.svg b/badges/interrogate_badge.svg
similarity index 100%
rename from interrogate_badge.svg
rename to badges/interrogate_badge.svg
diff --git a/binarycpython/tests/main.py b/binarycpython/tests/main.py
index 2c8111696dfd428c2505c9ef3644ee40e177b92d..4e6a1c292cd7ce11dabc1805b6ebb0c70b9a09d0 100644
--- a/binarycpython/tests/main.py
+++ b/binarycpython/tests/main.py
@@ -4,6 +4,7 @@ Main file for the tests. This file imports all the combined_test functions from
import unittest
+
from binarycpython.tests.test_c_bindings import *
from binarycpython.tests.test_custom_logging import *
from binarycpython.tests.test_distributions import *
diff --git a/binarycpython/tests/test_c_bindings.py b/binarycpython/tests/test_c_bindings.py
index 5796759832eedd7395647bcb726c4afb8a4c1bea..3e9029ad32e8ef4e07d167c06b77cec57d83138e 100644
--- a/binarycpython/tests/test_c_bindings.py
+++ b/binarycpython/tests/test_c_bindings.py
@@ -21,7 +21,7 @@ from binarycpython.utils.functions import (
verbose_print,
extract_ensemble_json_from_string,
is_capsule,
- Capturing
+ Capturing,
)
# https://docs.python.org/3/library/unittest.html
@@ -65,6 +65,7 @@ ensemble_filters_off {8} ensemble_filter_{9} 1 probability 0.1"
return argstring
+
#######################################################################################################################################################
### General run_system test
#######################################################################################################################################################
@@ -115,7 +116,8 @@ class test_run_system(unittest.TestCase):
### memaddr test
#######################################################################################################################################################
-# TODO: Make some assertion tests in c
+# TODO: Make some assertion tests in c
+
class test_return_store_memaddr(unittest.TestCase):
"""
diff --git a/binarycpython/tests/test_custom_logging.py b/binarycpython/tests/test_custom_logging.py
index 05792732464a1672bd6d9304ae8d64084ab779d9..717b4a7509f1753e31899ffcb85d1aa5cd5bd2e5 100644
--- a/binarycpython/tests/test_custom_logging.py
+++ b/binarycpython/tests/test_custom_logging.py
@@ -18,7 +18,7 @@ class test_custom_logging(unittest.TestCase):
def test_autogen_C_logging_code(self):
with Capturing() as output:
self._test_autogen_C_logging_code()
- print("\n".join(output))
+ # print("\n".join(output))
def _test_autogen_C_logging_code(self):
"""
@@ -51,7 +51,7 @@ class test_custom_logging(unittest.TestCase):
def test_binary_c_log_code(self):
with Capturing() as output:
self._test_binary_c_log_code()
- print("\n".join(output))
+ # print("\n".join(output))
def _test_binary_c_log_code(self):
"""
@@ -64,7 +64,8 @@ class test_custom_logging(unittest.TestCase):
input_2 = 'Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));'
output_2 = binary_c_log_code(input_2, verbose=1)
- test_value_2 = '#pragma push_macro("MAX")\n#pragma push_macro("MIN")\n#undef MAX\n#undef MIN\n#include "binary_c.h"\n#include "RLOF/RLOF_prototypes.h"\n\n// add visibility __attribute__ ((visibility ("default"))) to it \nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata);\nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata)\n{\n // struct stardata_t * stardata = (struct stardata_t *)x;\n Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));;\n}\n\n#undef MAX \n#undef MIN\n#pragma pop_macro("MIN")\n#pragma pop_macro("MAX") '
+ test_value_2 = '#pragma push_macro("Max")\n#pragma push_macro("Min")\n#undef Max\n#undef Min\n#include "binary_c.h"\n\n// add visibility __attribute__ ((visibility ("default"))) to it \nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata);\nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata)\n{\n // struct stardata_t * stardata = (struct stardata_t *)x;\n Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));;\n}\n\n#undef Max \n#undef Min\n#pragma pop_macro("Min")\n#pragma pop_macro("Max") '
+
self.assertEqual(
output_2,
test_value_2,
@@ -74,14 +75,15 @@ class test_custom_logging(unittest.TestCase):
def test_binary_c_write_log_code(self):
with Capturing() as output:
self._test_binary_c_write_log_code()
- print("\n".join(output))
+ # print("\n".join(output))
def _test_binary_c_write_log_code(self):
"""
Tests to see if writing the code to a file and reading that out again is the same
"""
- input_1 = '#pragma push_macro("MAX")\n#pragma push_macro("MIN")\n#undef MAX\n#undef MIN\n#include "binary_c.h"\n#include "RLOF/RLOF_prototypes.h"\n\n// add visibility __attribute__ ((visibility ("default"))) to it \nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata);\nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata)\n{\n // struct stardata_t * stardata = (struct stardata_t *)x;\n Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));;\n}\n\n#undef MAX \n#undef MIN\n#pragma pop_macro("MIN")\n#pragma pop_macro("MAX") '
+ input_1 = '#pragma push_macro("Max")\n#pragma push_macro("Min")\n#undef Max\n#undef Min\n#include "binary_c.h"\n\n// add visibility __attribute__ ((visibility ("default"))) to it \nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata);\nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata)\n{\n // struct stardata_t * stardata = (struct stardata_t *)x;\n Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));;\n}\n\n#undef Max \n#undef Min\n#pragma pop_macro("Min")\n#pragma pop_macro("Max") '
+
binary_c_write_log_code(
input_1,
os.path.join(binary_c_temp_dir, "test_binary_c_write_log_code.txt"),
@@ -103,7 +105,7 @@ class test_custom_logging(unittest.TestCase):
def test_from_binary_c_config(self):
with Capturing() as output:
self._test_from_binary_c_config()
- print("\n".join(output))
+ # print("\n".join(output))
def _test_from_binary_c_config(self):
"""
@@ -132,7 +134,7 @@ class test_custom_logging(unittest.TestCase):
def test_return_compilation_dict(self):
with Capturing() as output:
self._test_return_compilation_dict()
- print("\n".join(output))
+ # print("\n".join(output))
def _test_return_compilation_dict(self):
"""
@@ -153,7 +155,7 @@ class test_custom_logging(unittest.TestCase):
def test_create_and_load_logging_function(self):
with Capturing() as output:
self._test_create_and_load_logging_function()
- print("\n".join(output))
+ # print("\n".join(output))
def _test_create_and_load_logging_function(self):
"""
@@ -161,7 +163,7 @@ class test_custom_logging(unittest.TestCase):
"""
#
- input_1 = '#pragma push_macro("MAX")\n#pragma push_macro("MIN")\n#undef MAX\n#undef MIN\n#include "binary_c.h"\n#include "RLOF/RLOF_prototypes.h"\n\n// add visibility __attribute__ ((visibility ("default"))) to it \nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata);\nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata)\n{\n // struct stardata_t * stardata = (struct stardata_t *)x;\n Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));;\n}\n\n#undef MAX \n#undef MIN\n#pragma pop_macro("MIN")\n#pragma pop_macro("MAX") '
+ input_1 = '#pragma push_macro("MAX")\n#pragma push_macro("MIN")\n#undef MAX\n#undef MIN\n#include "binary_c.h"\n\n// add visibility __attribute__ ((visibility ("default"))) to it \nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata);\nvoid binary_c_API_function custom_output_function(struct stardata_t * stardata)\n{\n // struct stardata_t * stardata = (struct stardata_t *)x;\n Printf("MY_STELLAR_DATA %g %g %g %g\\n",((double)stardata->model.time),((double)stardata->star[0].mass),((double)stardata->model.probability),((double)stardata->model.dt));;\n}\n\n#undef MAX \n#undef MIN\n#pragma pop_macro("MIN")\n#pragma pop_macro("MAX") '
output_1 = create_and_load_logging_function(input_1, verbose=1)
self.assertTrue(isinstance(output_1[0], int), msg="memaddr is not an int")
diff --git a/binarycpython/tests/test_distributions.py b/binarycpython/tests/test_distributions.py
index 9a4bd344188e8176b37821d3c2d622d798c9b05a..caeb87ffe3119f48359edd64cc4a51088d91d3a2 100644
--- a/binarycpython/tests/test_distributions.py
+++ b/binarycpython/tests/test_distributions.py
@@ -8,6 +8,7 @@ from binarycpython.utils.distribution_functions import *
from binarycpython.utils.useful_funcs import calc_sep_from_period
from binarycpython.utils.functions import Capturing
+
class TestDistributions(unittest.TestCase):
"""
Unittest class
@@ -121,7 +122,9 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
+ msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance)
# extra test for k = -1
@@ -155,8 +158,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
# Extra test:
# M < M0
@@ -175,7 +182,7 @@ class TestDistributions(unittest.TestCase):
"""
perl_results = [
- 5.71196495365248,
+ 0, # perl value is actually 5.71196495365248
2.31977861075353,
0.143138195684851,
0.000717390363216896,
@@ -191,8 +198,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
# Extra tests:
self.assertEqual(
@@ -210,7 +221,7 @@ class TestDistributions(unittest.TestCase):
"""
perl_results = [
- 5.79767807698379,
+ 0, # perl value is actually 5.79767807698379 but that is not correct
2.35458895566605,
0.155713799148675,
0.000310689875361984,
@@ -226,8 +237,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
# extra test:
self.assertEqual(
@@ -292,21 +307,27 @@ class TestDistributions(unittest.TestCase):
input_1 = 0
self.assertRaises(ValueError, imf_chabrier2003, input_1)
- # for m=0.5
- m = 0.5
- self.assertLess(
- np.abs(imf_chabrier2003(m) - 0.581457346702825),
- self.tolerance,
- msg="Difference is bigger than the tolerance. Input mass = {}".format(m),
- )
+ masses = [0.1, 0.2, 0.5, 1, 2, 10, 15, 50]
+ perl_results = [
+ 5.64403964849588,
+ 2.40501495673496,
+ 0.581457346702825,
+ 0.159998782068074,
+ 0.0324898485372181,
+ 0.000801893469684309,
+ 0.000315578044662863,
+ 1.97918170035704e-05,
+ ]
+ python_results = [imf_chabrier2003(m) for m in masses]
- # For m = 2
- m = 2
- self.assertLess(
- np.abs(imf_chabrier2003(m) - 0.581457346702825),
- self.tolerance,
- msg="Difference is bigger than the tolerance. Input mass = {}".format(m),
- )
+ # GO over the results and check whether they are equal (within tolerance)
+ for i in range(len(python_results)):
+ msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+ perl_results[i], python_results[i], str(masses[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
def test_duquennoy1991(self):
with Capturing() as output:
@@ -345,8 +366,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for logper: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for logper: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
# Extra test:
self.assertTrue(
@@ -380,8 +405,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
def test_raghavan2010_binary_fraction(self):
with Capturing() as output:
@@ -402,8 +431,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
def test_Izzard2012_period_distribution(self):
with Capturing() as output:
@@ -463,8 +496,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass, per: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
def test_flatsections(self):
with Capturing() as output:
@@ -494,8 +531,12 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for q: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for q: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
def test_sana12(self):
with Capturing() as output:
@@ -746,8 +787,13 @@ class TestDistributions(unittest.TestCase):
# GO over the results and check whether they are equal (within tolerance)
for i in range(len(python_results)):
- msg = "Error: Value perl: {} Value python: {} for mass, mass2, per: {}".format(python_results[i], perl_results[i], str(input_lists[i]))
- self.assertLess(np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg)
+ msg = "Error: Value perl: {} Value python: {} for mass, mass2, per: {}".format(
+ perl_results[i], python_results[i], str(input_lists[i])
+ )
+ self.assertLess(
+ np.abs(python_results[i] - perl_results[i]), self.tolerance, msg=msg
+ )
+
if __name__ == "__main__":
unittest.main()
diff --git a/binarycpython/tests/test_functions.py b/binarycpython/tests/test_functions.py
index 5f105d00d1ed12f1e12e72c9169d13c5720e1c72..b576bf7f66ea5fa98a350c7a0084ab6bf79c18ee 100644
--- a/binarycpython/tests/test_functions.py
+++ b/binarycpython/tests/test_functions.py
@@ -45,7 +45,6 @@ class test_verbose_print(unittest.TestCase):
Unittests for verbose_print
"""
-
def test_print(self):
with Capturing() as output:
self._test_print()
@@ -243,7 +242,9 @@ class test_parse_binary_c_version_info(unittest.TestCase):
if parsed_info["macros"]["NUCSYN"] == "on":
self.assertIsNotNone(parsed_info["isotopes"])
- self.assertIsNotNone(parsed_info["nucleosynthesis_sources"])
+
+ if parsed_info["macros"]["NUCSYN_ID_SOURCES"] == "on":
+ self.assertIsNotNone(parsed_info["nucleosynthesis_sources"])
class test_output_lines(unittest.TestCase):
@@ -383,7 +384,6 @@ class test_get_arg_keys(unittest.TestCase):
Unittests for function get_arg_keys
"""
-
def test_1(self):
with Capturing() as output:
self._test_1()
diff --git a/binarycpython/tests/test_grid.py b/binarycpython/tests/test_grid.py
index c846ccd7ecb8cb5c2b49774d4c06b558c29c6e42..6bda6842c16fec4558a0e298c7a5216dfefdb4ce 100644
--- a/binarycpython/tests/test_grid.py
+++ b/binarycpython/tests/test_grid.py
@@ -3,6 +3,7 @@ Test cases for the grid
Tasks:
TODO: write tests for load_from_sourcefile
+ TODO: Set the temp_dir to grid_tests subdir. Should use that as an argument for the temp_dir() function
"""
import os
@@ -286,7 +287,9 @@ class test_Population(unittest.TestCase):
if binary_c_version_info["macros"]["NUCSYN"] == "on":
self.assertIsNotNone(binary_c_version_info["isotopes"])
- self.assertIsNotNone(binary_c_version_info["nucleosynthesis_sources"])
+
+ if binary_c_version_info["macros"]["NUCSYN_ID_SOURCES"] == "on":
+ self.assertIsNotNone(binary_c_version_info["nucleosynthesis_sources"])
def test__return_binary_c_defaults(self):
with Capturing() as output:
@@ -670,18 +673,18 @@ class test_grid_evolve(unittest.TestCase):
test_pop.set(failed_systems_threshold=4)
CUSTOM_LOGGING_STRING_WITH_EXIT = """
- Exit_binary_c(BINARY_C_NORMAL_EXIT, "testing exits. This is part of the testing, don't worry");
- Printf("TEST_CUSTOM_LOGGING_1 %30.12e %g %g %g %g\\n",
- //
- stardata->model.time, // 1
-
- // masses
- stardata->common.zero_age.mass[0], //
- stardata->common.zero_age.mass[1], //
-
- stardata->star[0].mass,
- stardata->star[1].mass
- );
+Exit_binary_c(BINARY_C_NORMAL_EXIT, "testing exits. This is part of the testing, don't worry");
+Printf("TEST_CUSTOM_LOGGING_1 %30.12e %g %g %g %g\\n",
+ //
+ stardata->model.time, // 1
+
+ // masses
+ stardata->common.zero_age.mass[0], //
+ stardata->common.zero_age.mass[1], //
+
+ stardata->star[0].mass,
+ stardata->star[1].mass
+);
"""
test_pop.set(C_logging_code=CUSTOM_LOGGING_STRING_WITH_EXIT)
@@ -717,6 +720,8 @@ class test_grid_evolve(unittest.TestCase):
test_pop = Population()
test_pop.set(amt_cores=2, verbosity=1, M_2=1, orbital_period=100000)
+ test_pop.set(failed_systems_threshold=4)
+ test_pop.set(C_logging_code=CUSTOM_LOGGING_STRING_WITH_EXIT)
resolution = {"M_1": 10, "q": 2}
@@ -749,7 +754,12 @@ class test_grid_evolve(unittest.TestCase):
condition="'random_var' in dir()", # This will raise an error because random_var is not defined.
)
- self.assertRaises(ValueError, test_pop.evolve)
+ # TODO: why should it raise this error? It should probably raise a valueerror when the limit is exceeded right?
+ # DEcided to turn it off for now because there is not raise VAlueError in that chain of functions.
+ # NOTE: Found out why this test was here. It is to do with the condition random_var in dir(), but I changed the behaviour from raising an error to continue. This has to do with the moe&distefano code that will loop over several multiplicities
+ # TODO: make sure the continue behaviour is what we actually want.
+
+ # self.assertRaises(ValueError, test_pop.evolve)
def test_grid_evolve_no_grid_variables(self):
with Capturing() as output:
@@ -887,12 +897,12 @@ class test_grid_evolve(unittest.TestCase):
analytics = test_pop.evolve()
- self.assertTrue(isinstance(test_pop.grid_ensemble_results['ensemble'], dict))
- self.assertNotEqual(test_pop.grid_ensemble_results['ensemble'], {})
+ self.assertTrue(isinstance(test_pop.grid_ensemble_results["ensemble"], dict))
+ self.assertNotEqual(test_pop.grid_ensemble_results["ensemble"], {})
- self.assertIn("number_counts", test_pop.grid_ensemble_results['ensemble'])
+ self.assertIn("number_counts", test_pop.grid_ensemble_results["ensemble"])
self.assertNotEqual(
- test_pop.grid_ensemble_results['ensemble']["number_counts"], {}
+ test_pop.grid_ensemble_results["ensemble"]["number_counts"], {}
)
def test_grid_evolve_2_threads_with_ensemble_comparing_two_methods(self):
@@ -1004,17 +1014,20 @@ class test_grid_evolve(unittest.TestCase):
ensemble_output_2 = merge_dicts(ensemble_output_2, ensemble_json)
- for key in ensemble_output_1['ensemble']["number_counts"]["stellar_type"]["0"]:
+ for key in ensemble_output_1["ensemble"]["number_counts"]["stellar_type"]["0"]:
self.assertIn(key, ensemble_output_2["number_counts"]["stellar_type"]["0"])
# compare values
self.assertLess(
np.abs(
- ensemble_output_1['ensemble']["number_counts"]["stellar_type"]["0"][key]
+ ensemble_output_1["ensemble"]["number_counts"]["stellar_type"]["0"][
+ key
+ ]
- ensemble_output_2["number_counts"]["stellar_type"]["0"][key]
),
1e-8,
)
+
if __name__ == "__main__":
unittest.main()
diff --git a/binarycpython/tests/test_grid_options_defaults.py b/binarycpython/tests/test_grid_options_defaults.py
index bc941176181ac4526d7bb59f10a0c02600ef1e0e..8986e1ada77ac6f10c18e2064b31c7b46b66dc62 100644
--- a/binarycpython/tests/test_grid_options_defaults.py
+++ b/binarycpython/tests/test_grid_options_defaults.py
@@ -1,11 +1,18 @@
"""
Unittests for grid_options_defaults module
"""
-
+import os
import unittest
-from binarycpython.utils.grid_options_defaults import *
-from binarycpython.utils.functions import *
+from binarycpython.utils.functions import (
+ temp_dir,
+ Capturing,
+)
+from binarycpython.utils.grid_options_defaults import (
+ write_grid_options_to_rst_file,
+ grid_options_help,
+ grid_options_description_checker,
+)
binary_c_temp_dir = temp_dir()
diff --git a/binarycpython/tests/test_hpc_functions.py b/binarycpython/tests/test_hpc_functions.py
index ddba58b7b5a925d0a2dcaef565a9608b37559fb9..e5fe16c34ea86965592827255a6649a54de2c149 100644
--- a/binarycpython/tests/test_hpc_functions.py
+++ b/binarycpython/tests/test_hpc_functions.py
@@ -4,4 +4,4 @@ Unittests for hpc_functions module
from binarycpython.utils.hpc_functions import *
-# TODO: write tests for hpc functions
\ No newline at end of file
+# TODO: write tests for hpc functions
diff --git a/binarycpython/tests/test_plot_functions.py b/binarycpython/tests/test_plot_functions.py
index c8e9a1304432c543a7a0e79b3ebfa3250694d7e5..43b716e95d811cf78d88ca8b64a4aabdb42b391f 100644
--- a/binarycpython/tests/test_plot_functions.py
+++ b/binarycpython/tests/test_plot_functions.py
@@ -17,6 +17,7 @@ from binarycpython.utils.functions import Capturing
# def test_1(self):
# pass
+
class test_color_by_index(unittest.TestCase):
"""
Unittests for function color_by_index
diff --git a/binarycpython/tests/test_run_system_wrapper.py b/binarycpython/tests/test_run_system_wrapper.py
index 87f0a5e0d54b59bacf821643ab06372113e9518b..ef4cc754d43de33d02c0cf748caa16f2e9147a30 100644
--- a/binarycpython/tests/test_run_system_wrapper.py
+++ b/binarycpython/tests/test_run_system_wrapper.py
@@ -2,7 +2,4 @@
Unittests for run_system_wrapper
"""
-from binarycpython.utils.run_system_wrapper import *
-from binarycpython.utils.functions import *
-
-# TODO: write tests for run_system_wrapper
\ No newline at end of file
+# TODO: write tests for run_system_wrapper
diff --git a/binarycpython/tests/test_spacing_functions.py b/binarycpython/tests/test_spacing_functions.py
index cd50b86a177458aa2384f772bf68dde0fdc6d7ee..a61f7251225a6e92396682072d7b80c97048291e 100644
--- a/binarycpython/tests/test_spacing_functions.py
+++ b/binarycpython/tests/test_spacing_functions.py
@@ -2,11 +2,12 @@
Unittests for spacing_functions module
"""
-
import unittest
import numpy as np
-from binarycpython.utils.spacing_functions import *
-from binarycpython.utils.functions import *
+
+from binarycpython.utils.spacing_functions import const
+from binarycpython.utils.functions import Capturing
+
class test_spacing_functions(unittest.TestCase):
"""
@@ -27,3 +28,7 @@ class test_spacing_functions(unittest.TestCase):
(const_return == np.linspace(1, 10, 10)).all(),
msg="Output didn't contain SINGLE_STAR_LIFETIME",
)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/binarycpython/tests/test_stellar_types.py b/binarycpython/tests/test_stellar_types.py
index 9ed515f7a726aecceda6c51621606d95994f90c0..84d3e5ee35edc912fdccadaf4c5c03956a54a1de 100644
--- a/binarycpython/tests/test_stellar_types.py
+++ b/binarycpython/tests/test_stellar_types.py
@@ -1,8 +1,3 @@
"""
Unittests for stellar_types module
"""
-
-import unittest
-
-from binarycpython.utils.stellar_types import *
-from binarycpython.utils.functions import *
diff --git a/binarycpython/tests/test_useful_funcs.py b/binarycpython/tests/test_useful_funcs.py
index 64531c4b3bdefb2a2ebc208dd6d3833e3d90a3ca..bbab35f7a292118dcf06e6f081cc04777265e111 100644
--- a/binarycpython/tests/test_useful_funcs.py
+++ b/binarycpython/tests/test_useful_funcs.py
@@ -5,8 +5,18 @@ Unittests for useful_funcs module
import unittest
import numpy as np
-from binarycpython.utils.useful_funcs import *
-from binarycpython.utils.functions import *
+from binarycpython.utils.functions import (
+ Capturing,
+)
+
+from binarycpython.utils.useful_funcs import (
+ zams_collision,
+ rzams,
+ roche_lobe,
+ ragb,
+ calc_sep_from_period,
+ calc_period_from_sep,
+)
# class test_(unittest.TestCase):
# """
diff --git a/binarycpython/utils/custom_logging_functions.py b/binarycpython/utils/custom_logging_functions.py
index f869835b009d5421e9305a16103779e8b89c8aaa..e3be2e16ba91c5bfbcaae024c8fe48fdf0aa5156 100644
--- a/binarycpython/utils/custom_logging_functions.py
+++ b/binarycpython/utils/custom_logging_functions.py
@@ -132,12 +132,11 @@ def binary_c_log_code(code: str, verbose: int = 0) -> str:
# Create code
custom_logging_function_string = """\
-#pragma push_macro(\"MAX\")
-#pragma push_macro(\"MIN\")
-#undef MAX
-#undef MIN
-#include \"binary_c.h\"
-#include \"RLOF/RLOF_prototypes.h\"
+#pragma push_macro(\"Max\")
+#pragma push_macro(\"Min\")
+#undef Max
+#undef Min
+#include "binary_c.h"
// add visibility __attribute__ ((visibility ("default"))) to it
void binary_c_API_function custom_output_function(struct stardata_t * stardata);
@@ -147,10 +146,10 @@ void binary_c_API_function custom_output_function(struct stardata_t * stardata)
{};
}}
-#undef MAX
-#undef MIN
-#pragma pop_macro(\"MIN\")
-#pragma pop_macro(\"MAX\")\
+#undef Max
+#undef Min
+#pragma pop_macro(\"Min\")
+#pragma pop_macro(\"Max\")\
""".format(
code
)
@@ -204,6 +203,7 @@ def from_binary_c_config(config_file: str, flag: str) -> str:
# convert and chop off newline
res = res.decode("utf-8").rstrip()
+ print(res)
return res
@@ -255,7 +255,8 @@ def return_compilation_dict(verbose: int = 0) -> dict:
bincincdirs = from_binary_c_config(BINARY_C_CONFIG, "incdirs")
# combine
- binclibs = " {} {} {}".format(libdirs, libbinary_c, binclibs)
+ # binclibs = " {} {} {}".format(libdirs, libbinary_c, binclibs)
+ binclibs = " {} {} {}".format(libdirs, binclibs, libbinary_c)
# setup defaults:
defaults = {
@@ -359,7 +360,7 @@ def compile_shared_lib(
)
)
res = subprocess.check_output("{command}".format(command=command), shell=True)
-
+ print(res)
if verbose > 0:
if res:
print("Output of compilation command:\n{}".format(res))
@@ -389,12 +390,12 @@ def create_and_load_logging_function(
else:
tmp_dir = custom_tmp_dir
- custom_logging_dir = os.path.join(tmp_dir, 'custom_logging')
+ custom_logging_dir = os.path.join(tmp_dir, "custom_logging")
# Create the subdir for the custom_logging code
os.makedirs(custom_logging_dir, exist_ok=True)
- #
+ #
library_name = os.path.join(
custom_logging_dir, "libcustom_logging_{}.so".format(uuid.uuid4().hex)
)
diff --git a/binarycpython/utils/distribution_functions.py b/binarycpython/utils/distribution_functions.py
index 4cb4f17fde4d7647cf4fdd4f6ad5690f09ba07fa..0fb0037c603bc5570a1ed6f26c38a1f61b825e8d 100644
--- a/binarycpython/utils/distribution_functions.py
+++ b/binarycpython/utils/distribution_functions.py
@@ -11,18 +11,29 @@ There are distributions for the following parameters:
- binary fraction
Tasks:
- - 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 global constants stuff
+ - 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 eccentricity distribution: thermal
- TODO: Add SFH distributions depending on redshift
- TODO: Add metallicity distributions depending on redshift
- TODO: Add initial rotational velocity distributions
+ - TODO: make an n-part powerlaw thats general enough to fix the three part and the 4 part
"""
+import gc
import math
+import json
+
+from typing import Union
+
import numpy as np
-from typing import Optional, Union
-from binarycpython.utils.useful_funcs import calc_period_from_sep
+
+from binarycpython.utils.useful_funcs import calc_period_from_sep, calc_sep_from_period
+from binarycpython.utils.functions import verbose_print
+from binarycpython.utils.grid_options_defaults import (
+ _MS_VERBOSITY_LEVEL,
+ _MS_VERBOSITY_INTERPOLATOR_LEVEL,
+)
###
# File containing probability distributions
@@ -33,7 +44,9 @@ distribution_constants = {} # To store the constants in
def prepare_dict(global_dict: dict, list_of_sub_keys: list) -> None:
"""
- Function that makes sure that the global dict is prepared to have a value set there. This dictionary will store values and factors for the distribution functions, so that they dont have to be calculated each time.
+ Function that makes sure that the global dict is prepared to have a value set there.
+ This dictionary will store values and factors for the distribution functions,
+ so that they dont have to be calculated each time.
Args:
global_dict: globablly acessible dictionary where factors are stored in
@@ -44,7 +57,8 @@ def prepare_dict(global_dict: dict, list_of_sub_keys: list) -> None:
# This loop almost mimics a recursive loop into the dictionary.
# It checks whether the first key of the list is present, if not; set it with an empty dict.
- # Then it overrides itself to be that (new) item, and goes on to do that again, until the list exhausted
+ # Then it overrides itself to be that (new) item, and goes on to do that again, until the list
+ # exhausted
for k in list_of_sub_keys:
# If the sub key doesnt exist then make an empty dict
if not internal_dict_value.get(k, None):
@@ -171,8 +185,8 @@ def powerlaw(
# Handle faulty value
if k == -1:
- print("wrong value for k")
- raise ValueError
+ msg = "wrong value for k"
+ raise ValueError(msg)
if (x < min_val) or (x > max_val):
print("input value is out of bounds!")
@@ -297,7 +311,7 @@ def three_part_powerlaw(
#
if m < m0:
- prob = 0 # Below lower bound
+ prob = 0 # Below lower bound TODO: make this clear.
elif m0 < m <= m1:
prob = three_part_powerlaw_constants[0] * (m ** p1) # Between M0 and M1
elif m1 < m <= m2:
@@ -393,7 +407,7 @@ def gaussian(
# normalize over given range
# TODO: add loading into global var
normalisation = gaussian_normalizing_const(mean, sigma, gmin, gmax)
- prob = normalisation * gaussian_func(x, mean, sigma)
+ prob = gaussian_func(x, mean, sigma) / normalisation
return prob
@@ -405,7 +419,9 @@ def gaussian(
def Kroupa2001(m: Union[int, float], newopts: dict = None) -> Union[int, float]:
"""
- Probability distribution function for kroupa 2001 IMF, where the default values to the three_part_powerlaw are: default = {"m0": 0.1, "m1": 0.5, "m2": 1, "mmax": 100, "p1": -1.3, "p2": -2.3,"p3": -2.3}
+ Probability distribution function for kroupa 2001 IMF, where the default values to the
+ three_part_powerlaw are:
+ default = {"m0": 0.1, "m1": 0.5, "m2": 1, "mmax": 100, "p1": -1.3, "p2": -2.3,"p3": -2.3}
Args:
m: mass to evaluate the distribution at
@@ -578,8 +594,8 @@ def imf_chabrier2003(m: Union[int, float]) -> Union[int, float]:
chabrier_a2 = 4.43e-2
chabrier_x = -1.3
if m <= 0:
- print("below bounds")
- raise ValueError
+ msg = "below bounds"
+ raise ValueError(msg)
if 0 < m < 1.0:
A = 0.158
dm = math.log10(m) - chabrier_logmc
@@ -900,21 +916,1382 @@ def flatsections(x: float, opts: dict) -> Union[float, int]:
# Star formation histories
########################################################################
+
def cosmic_SFH_madau_dickinson2014(z):
"""
Cosmic star formation history distribution from Madau & Dickonson 2014 (https://arxiv.org/pdf/1403.0007.pdf)
-
+
Args:
z: redshift
-
+
Returns:
Cosmic star formation rate in Solarmass year^-1 megaparsec^-3
"""
- CSFH = 0.015 * ((1+z)**2.7)/(1+(((1+z)/2.9)**5.6))
+ CSFH = 0.015 * ((1 + z) ** 2.7) / (1 + (((1 + z) / 2.9) ** 5.6))
return CSFH
+
########################################################################
# Metallicity distributions
########################################################################
+
+
+########################################################################
+# Moe & DiStefano 2017 functions
+#
+# The code below are functions that are used to set up and interpolate
+# on the Moe & DiStefano 2017 data. The interpolators take the last
+# known value if we try to interpolate outside of the tables.
+# There are still some open tasks and improvements that can be made:
+#
+# TODO: check all the raise ValueErrors to make them more appropriate
+# TODO: Put the json checking stuff in a different function
+# TODO: Clean up the logging
+# TODO: Solve the memory issues that are present.
+# Are the interpolators not cleaned?
+# TODO: Parallellize the setting up of the interpolators
+# TODO: Generalize the code such that we can input other/newer tables
+
+
+########################################################################
+
+import py_rinterpolate
+
+# Global dictionary to store values in
+Moecache = {}
+
+def poisson(lambda_val, n, nmax=None, verbosity=0):
+ """
+ Function that calculates the poisson value and normalizes
+ TODO: improve the description
+ """
+
+ cachekey = "{} {} {}".format(lambda_val, n, nmax)
+
+ if distribution_constants.get("poisson_cache", None):
+ if distribution_constants["poisson_cache"].get(cachekey, None):
+ p_val = distribution_constants["poisson_cache"][cachekey]
+
+ verbose_print(
+ "\tM&S: found cached value for poisson({}, {}, {}): {}".format(
+ lambda_val, n, nmax, p_val
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ return p_val
+
+ # Poisson distribution : note, n can be zero
+ #
+ # nmax is the truncation : if set, we normalize
+ # correctly.
+ p_val = _poisson(lambda_val, n)
+
+ if nmax:
+ I_poisson = 0
+ for i in range(nmax + 1):
+ I_poisson += _poisson(lambda_val, i)
+ p_val = p_val / I_poisson
+
+ # Add to cache
+ if not distribution_constants.get("poisson_cache", None):
+ distribution_constants["poisson_cache"] = {}
+ distribution_constants["poisson_cache"][cachekey] = p_val
+
+ verbose_print(
+ "\tM&S: Poisson({}, {}, {}): {}".format(lambda_val, n, nmax, p_val),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ return p_val
+
+
+def _poisson(lambda_val, n):
+ """
+ Function to return the poisson value
+ """
+
+ return (lambda_val ** n) * np.exp(-lambda_val) / (1.0 * math.factorial(n))
+
+def get_max_multiplicity(multiplicity_array):
+ """
+ Function to get the maximum multiplicity
+ """
+
+ max_multiplicity = 0
+ for n in range(4):
+ if multiplicity_array[n] > 0:
+ max_multiplicity = n + 1
+ return max_multiplicity
+
+def merge_multiplicities(result_array, max_multiplicity, verbosity=0):
+ """
+ Function to fold the multiplicities higher than the max_multiplicity onto the max_multiplicity
+
+ if max_multiplicity == 1:
+ All the multiplicities are folded onto multiplicity == 1. This will always total to 1
+ if max_multiplicity == 2:
+ The multiplicity fractions of the triple and quadruples are folded onto that of the binary multiplicity fraction
+ if max_multiplicity == 3:
+ The multiplicity fractions of the quadruples are folded onto that of the triples
+ """
+
+ if not max_multiplicity in range(1, 5):
+ msg = "\tM&S: merge_multiplicities: max_multiplicity has to be between 1 and 4. It is {} now".format(
+ max_multiplicity
+ )
+ verbose_print(
+ msg,
+ verbosity,
+ 0,
+ )
+ raise ValueError(msg)
+
+ # Fold multiplicities:
+ verbose_print(
+ "\tM&S: merge_multiplicities: Merging multiplicities with initial array {} and max multiplicity {}".format(result_array, max_multiplicity),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ for i in range(max_multiplicity, len(result_array))[::-1]:
+ result_array[i-1] += result_array[i]
+ result_array[i] = 0
+ verbose_print(
+ "\tM&S: merge_multiplicities: Merging multiplicities to new array {}".format(result_array),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ return result_array
+
+def normalize_dict(result_dict, verbosity=0):
+ """
+ Function to normalize a dictionary
+ """
+
+ sum_result = sum([result_dict[key] for key in result_dict.keys()])
+ for key in result_dict.keys():
+ result_dict[key] = result_dict[key] / sum_result
+ return result_dict
+
+def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
+ """
+ Function that creates a list of probability fractions and
+ normalizes and merges them according to the users choice.
+
+
+ TODO: make an extrapolation functionality in this. log10(1.6e1)
+ is low, we can probably go a bit further
+
+ The default result that is returned when sampling the mass outside
+ of the mass range is now the last known value
+
+ Returns a list of multiplicity fractions for a given input of mass
+ """
+
+ # Use the global Moecache
+ global Moecache
+
+ multiplicity_modulator_array = np.array(options["multiplicity_modulator"]) # Modulator array
+
+ # Check for length
+ if not len(multiplicity_modulator_array)==4:
+ msg = "Multiplicity modulator has to have 4 elements. Now it is {}, len: {}".format(multiplicity_modulator_array, len(multiplicity_modulator_array))
+ verbose_print(
+ msg,
+ verbosity,
+ 0,
+ )
+ raise ValueError(msg)
+
+ # Set up some arrays
+ full_fractions_array = np.zeros(4) # Meant to contain the real fractions
+ weighted_fractions_array = np.zeros(4) # Meant to contain the fractions multiplied by the multiplicity modulator
+
+ # Get max multiplicity
+ max_multiplicity = get_max_multiplicity(multiplicity_modulator_array)
+
+ # ... it's better to interpolate the multiplicity and then
+ # use a Poisson distribution to calculate the fractions
+ # (this is more accurate)
+
+ # Set up the multiplicity interpolator
+ if not Moecache.get("rinterpolator_multiplicity", None):
+ Moecache["rinterpolator_multiplicity"] = py_rinterpolate.Rinterpolate(
+ table=Moecache["multiplicity_table"], # Contains the table of data
+ nparams=1, # logM1
+ ndata=4, # The amount of datapoints (the parameters that we want to interpolate)
+ verbosity=verbosity - (_MS_VERBOSITY_INTERPOLATOR_LEVEL - 1),
+ )
+
+ if options["multiplicity_model"] == "Poisson":
+ multiplicity = Moecache["rinterpolator_multiplicity"].interpolate(
+ [np.log10(options["M1"])]
+ )[0]
+
+ # Fill the multiplicity array
+ for n in range(4):
+ full_fractions_array[n] = poisson(multiplicity, n, 3, verbosity)
+
+ # Normalize it so it fills to one when taking all the multiplicities:
+ full_fractions_array = full_fractions_array/np.sum(full_fractions_array)
+
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: using model {}: full_fractions_array: {}".format("Poisson", full_fractions_array),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ elif options["multiplicity_model"] == "data":
+ # use the fractions calculated from Moe's data directly
+ #
+ # note that in this case, there are no quadruples: these
+ # are combined with triples
+
+ # Fill with the raw values
+ for n in range(3):
+ full_fractions_array[n] = (
+ Moecache["rinterpolator_multiplicity"].interpolate(
+ [np.log10(options["M1"])]
+ )[n + 1])
+
+ # Set last value
+ full_fractions_array[3] = 0.0 # no quadruples
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: using model {}: full_fractions_array: {}".format("data", full_fractions_array),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Normalisation:
+ if options["normalize_multiplicities"] == "raw":
+ # Don't multiply by the multiplicity_array, but do give a fractions array
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Not normalizing (using raw results): results: {}".format(full_fractions_array),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ result = full_fractions_array
+
+ elif options["normalize_multiplicities"] == "norm":
+ # Multiply the full_multiplicity_fraction array by the multiplicity_multiplier_array, creating a weighted fractions array
+ weighted_fractions_array = full_fractions_array * multiplicity_modulator_array
+
+ # Normalize this so it is intotal 1:
+ result = weighted_fractions_array/np.sum(weighted_fractions_array)
+
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Normalizing with {}. result: {}".format("norm", result),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ elif options["normalize_multiplicities"] == "merge":
+ # We first take the full multiplicity array
+ # (i.e. not multiplied by multiplier) and do the merging
+ result = merge_multiplicities(full_fractions_array, max_multiplicity, verbosity=verbosity)
+
+ # Then normalize to be sure
+ result = result/np.sum(result)
+
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Normalizing with {}, max_multiplicity={} result={}".format("merge", max_multiplicity, result),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: {}".format(str(result)),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # return array reference
+ return result
+
+def build_q_table(options, m, p, verbosity=0):
+ ############################################################
+ #
+ # Build an interpolation table for q, given a mass and
+ # orbital period.
+ #
+ # $m and $p are labels which determine which system(s)
+ # to look up from Moe's data:
+ #
+ # $m can be M1, M2, M3, M4, or if set M1+M2 etc.
+ # $p can be P, P2, P3
+ #
+ # The actual values are in $opts:
+ #
+ # mass is in $opts->{$m}
+ # period is $opts->{$p}
+ #
+ # Since the information from the table for M&S is independent of any choice we make,
+ # we need to take into account that for example our choice of minimum mass leads to
+ # a minimum q_min that is not the same as in the table
+ # We should ignore those parts of the table and renormalize.
+ # If we are below the lowest value of qmin in the table we need to extrapolate the data
+ #
+ # Anyway, the goal of this function is to provide some extrapolated values for q when we should sample outside of the boundaries
+ ############################################################
+
+ # We can check if we have a cached value for this already:
+ # TODO: fix this cache check.
+ incache = False
+ if Moecache.get("rinterpolator_q_metadata", None):
+ if (Moecache["rinterpolator_q_metadata"].get(m, None)) and (
+ Moecache["rinterpolator_q_metadata"].get(p, None)
+ ):
+ if (Moecache["rinterpolator_q_metadata"][m] == options[m]) and (
+ Moecache["rinterpolator_q_metadata"][p] == options[p]
+ ):
+ incache = True
+
+ verbose_print(
+ "\tM&S: build_q_table: Found cached values for m={} p={}".format(
+ options[m], options[p]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ else:
+ verbose_print(
+ "\tM&S: build_q_table: Cached values for different m={} p={}. Freeing current table and making new table".format(
+ options[m], options[p]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ Moecache["rinterpolator_q_given_{}_log10{}".format(m, p)].destroy()
+ gc.collect()
+
+
+ #
+ if not incache:
+ # trim and/or expand the table to the range $qmin to $qmax.
+
+ # qmin is set by the minimum stellar mass : below this
+ # the companions are planets
+ # qmin = options["ranges"]["M"][
+ # 0
+ # ] # TODO: this lower range must not be lower than Mmin.
+ # print("build_q_table qmin: {}".format(qmin))
+ qmin = options['Mmin']/options['M1']
+ print("build_q_table qmin: {}".format(qmin))
+
+ # qmax = maximum_mass_ratio_for_RLOF(options[m], options[p])
+ # TODO: change this to the above
+ qmax = 1
+
+ # qdata contains the table that we modify: we get
+ # the original data by interpolating Moe's table
+ qdata = {}
+ can_renormalize = 1
+
+ qeps = 1e-8 # small number but such that qeps+1 != 1
+ if qeps + 1 == 1.0:
+ print("qeps (= {}) +1 == 1. Make qeps larger".format(qeps))
+
+ if qmin >= qmax:
+ # there may be NO binaries in this part of the parameter space:
+ # in which case, set up a table with lots of zero in it
+
+ qdata = {0: 0, 1: 0}
+ can_renormalize = 0
+
+ else:
+ # qmin and qmax mean we'll get something non-zero
+ can_renormalize = 1
+
+ # require extrapolation sets whether we need to extrapolate
+ # at the low and high ends
+ require_extrapolation = {}
+
+ if qmin >= 0.15:
+ # qmin is inside Moe's table : this is easy,
+ # we just keep points from qmin at the low
+ # end to qmax at the high end.
+ require_extrapolation["low"] = 0
+ require_extrapolation[
+ "high"
+ ] = 1 # TODO: shouldnt the extrapolation need to happen if qmax > 0.95
+ qdata[qmin] = Moecache["rinterpolator_q"].interpolate(
+ [np.log10(options[m]), np.log10(options[p]), qmin]
+ )[0]
+
+ for q in np.arange(0.15, 0.950001, 0.1):
+ if (q >= qmin) and (q <= qmax):
+ qdata[q] = Moecache["rinterpolator_q"].interpolate(
+ [np.log10(options[m]), np.log10(options[p]), q]
+ )[0]
+ else:
+ require_extrapolation["low"] = 1
+ require_extrapolation["high"] = 1
+ if qmax < 0.15:
+ # qmax < 0.15 which is off the edge
+ # of the table. In this case, choose
+ # two points at q=0.15 and 0.16 and interpolate
+ # at these in case we want to extrapolate.
+ for q in [0.15, 0.16]:
+ qdata[q] = Moecache["rinterpolator_q"].interpolate(
+ [np.log10(options[m]), np.log10(options[p]), q]
+ )[0]
+ else:
+ # qmin < 0.15 and qmax > 0.15, so we
+ # have to generate Moe's table for
+ # q = 0.15 (i.e. 0.1 to 0.2) to 0.95 (0.9 to 1)
+ # as a function of M1 and orbital period,
+ # to obtain the q distribution data.
+
+ for q in np.arange(0.15, np.min([0.950001, qmax + 0.0001]), 0.1):
+ val = Moecache["rinterpolator_q"].interpolate(
+ [np.log10(options[m]), np.log10(options[p]), q]
+ )[0]
+ qdata[q] = val
+
+ # just below qmin, if qmin>qeps, we want nothing
+ if qmin - 0.15 > qeps:
+ q = qmin - qeps
+ qdata[q] = 0
+ require_extrapolation["low"] = 0
+
+ # just above qmax, if qmax<1, we want nothing
+ if qmax < 0.95:
+ q = qmax + qeps
+ qdata[q] = 0
+ require_extrapolation["high"] = 0
+
+ # sorted list of qs
+ qs = sorted(qdata.keys())
+
+ if len(qs) == 0:
+ msg = "No qs found error"
+ raise ValueError(msg)
+
+ elif len(qs) == 1:
+ # only one q value : pretend there are two
+ # with a flat distribution up to 1.0.
+ if qs[0] == 1.0:
+ qs[0] = 1.0 - 1e-6
+ qs[1] = 1
+ qdata[qs[0]] = 1
+ qdata[qs[1]] = 1
+ else:
+ qs[1] = 1.0
+ qdata[qs[1]] = qs[0]
+
+ # We actually should do the extrapolation now.
+ else:
+ # Loop over both the lower end and the upper end
+ for pre in ["low", "high"]:
+ if require_extrapolation[pre] == 0:
+ continue
+ else:
+ sign = -1 if pre == "low" else 1
+ end_index = 0 if pre == "low" else len(qs) - 1
+ indices = (
+ [0, 1] if pre == "low" else [len(qs) - 1, len(qs) - 2]
+ ) # Based on whether we do the high or low end we need to use two different indices
+ method = options.get(
+ "q_{}_extrapolation_method".format(pre), None
+ )
+ qlimit = qmin if pre == "low" else qmax
+
+ verbose_print(
+ "\tM&S: build_q_table: Extrapolating: Q: {} method: {}, indices: {} End index: {}".format(
+ pre, method, indices, end_index
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # truncate the distribution
+ qdata[max(0.0, min(1.0, qlimit + sign * qeps))] = 0
+
+ if method == None:
+ # no extrapolation : just interpolate between 0.10 and 0.95
+ verbose_print(
+ "\tM&S: build_q_table: using no extrapolations".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ continue
+ elif method == "flat":
+ # use the end value value and extrapolate it
+ # with zero slope
+ qdata[qlimit] = qdata[qs[end_index]]
+ verbose_print(
+ "\tM&S: build_q_table: using constant extrapolation".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ elif method == "linear":
+ qdata[qlimit] = linear_extrapolation_q(
+ qs=qs,
+ indices=indices,
+ qlimit=qlimit,
+ qdata=qdata,
+ end_index=end_index,
+ verbosity=verbosity,
+ )
+
+ verbose_print(
+ "\tM&S: build_q_table: using linear extrapolation".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ if pre=='low':
+ below_qlimit = qlimit-qeps
+ if below_qlimit > 0:
+ qdata[below_qlimit] = 0
+ qdata[0] = 0
+ verbose_print(
+ "\tM&S: build_q_table: using linear extrapolation and setting the points below the lower q bound ({}) to 0 ".format(qlimit),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ elif method == "plaw2":
+ qdata[qlimit] = powerlaw_extrapolation_q(
+ qs=qs, indices=indices, qdata=qdata, verbosity=verbosity
+ )
+
+ verbose_print(
+ "\tM&S: build_q_table: using powerlaw extrapolation".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ elif method == "nolowq":
+ newq = 0.05
+ qdata[newq] = 0
+ verbose_print(
+ "\tM&S: build_q_table: setting lowq to 0".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ elif method=="poly":
+ # TODO: consider implementing the poly method (see perl version)
+ raise ValueError("M&S: build_q_table: Method 'poly' not implemented")
+
+ else:
+ msg = "\tM&S: build_q_table: Error no other methods available. The chosen method ({}) does not exist!".format(
+ method
+ )
+ verbose_print(
+ msg,
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ raise ValueError(msg)
+
+ # regenerate qs in new table. This is now the updated list of qs where we have some extrapolated numbers
+ tmp_table = []
+ for q in sorted(qdata.keys()):
+ tmp_table.append([q, qdata[q]])
+
+ # Make an interpolation table to contain our modified data
+ q_interpolator = py_rinterpolate.Rinterpolate(
+ table=tmp_table,
+ nparams=1,
+ ndata=1, # Contains the table of data # q #
+ verbosity=verbosity - (_MS_VERBOSITY_INTERPOLATOR_LEVEL - 1),
+ )
+ verbose_print(
+ "\tM&S: build_q_table: Created a new Q table",
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ if can_renormalize:
+ verbose_print(
+ "\tM&S: build_q_table: Renormalizing table",
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # now we integrate and renormalize (if the table is not all zero)
+ I = get_integration_constant_q(q_interpolator, tmp_table, qdata, verbosity=verbosity)
+
+ if I > 0:
+ # normalize to 1.0 by dividing the data by 1.0/$I
+ q_interpolator.multiply_table_column(1, 1.0 / I)
+
+ # test this
+ new_I = get_integration_constant_q(q_interpolator, tmp_table, qdata, verbosity=verbosity)
+
+ # fail if error in integral > 1e-6 (should be ~ machine precision)
+ if abs(1.0 - new_I) > 1e-6:
+ verbose_print(
+ "\tM&S: build_q_table: Error: > 1e-6 in q probability integral: {}".format(
+ I
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # set this new table in the cache
+ Moecache["rinterpolator_q_given_{}_log10{}".format(m, p)] = q_interpolator
+ verbose_print(
+ "\tM&S: build_q_table: stored q_interpolater as {}".format(
+ "rinterpolator_q_given_{}_log10{}".format(m, p)
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Store the values for which this table was set up in the dict
+ if not Moecache.get("rinterpolator_q_metadata", None):
+ Moecache["rinterpolator_q_metadata"] = {}
+ Moecache["rinterpolator_q_metadata"][m] = options[m]
+ Moecache["rinterpolator_q_metadata"][p] = options[p]
+
+
+def powerlaw_extrapolation_q(qdata, qs, indices, verbosity=0):
+ """
+ Function to do the powerlaw extrapolation at the lower end of the q range
+ """
+ newq = 0.05
+
+ # use a power-law extrapolation down to q=0.05, if possible
+ if (qdata[qs[indices[0]]] == 0.0) and (qdata[qs[indices[1]]] == 0.0):
+ # not possible
+ return 0
+
+ else:
+ slope = (np.log10(qdata[qs[indices[1]]]) - np.log10(qdata[qs[indices[0]]])) / (
+ np.log10(qs[indices[1]]) - np.log10(qs[indices[0]])
+ )
+ intercept = np.log10(qdata[qs[indices[0]]]) - slope * np.log10(qs[indices[0]])
+
+ return slope * newq + intercept
+
+
+def linear_extrapolation_q(qs, indices, qlimit, qdata, end_index, verbosity=0):
+ """
+ Function to do the linear extrapolation for q.
+ """
+
+ # linear extrapolation
+ dq = qs[indices[1]] - qs[indices[0]]
+
+ if dq == 0:
+ verbose_print(
+ "\tM&S: build_q_table: linear dq=0".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # No change
+ return qs[end_index]
+ else:
+ slope = (qdata[qs[indices[1]]] - qdata[qs[indices[0]]]) / dq
+
+ intercept = qdata[qs[indices[0]]] - slope * qs[indices[0]]
+ qdata[qlimit] = max(0.0, slope * qlimit + intercept)
+ verbose_print(
+ "\tM&S: build_q_table: linear Slope: {} intercept: {} dn/dq({}) = {}".format(
+ slope, intercept, qlimit, qdata[qlimit]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ return max(0.0, slope * qlimit + intercept)
+
+
+def get_integration_constant_q(q_interpolator, tmp_table, qdata, verbosity=0):
+ """
+ Function to integrate the q interpolator and return the integration constant
+ """
+
+ dq = 1e-3 # resolution of the integration/renormalization
+ I = 0
+
+ # integrate: note that the value of the integral is
+ # meaningless to within a factor (which depends on $dq)
+ for q in np.arange(0, 1 + 2e-6, dq):
+ x = q_interpolator.interpolate([q])
+ if len(x) == 0:
+ msg = "\tM&S: build_q_table: Q interpolator table interpolation failed.\n\t\ttmp_table = {}\n\t\tq_data = {}".format(
+ str(tmp_table), str(qdata)
+ )
+ verbose_print(
+ msg,
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ raise ValueError(msg)
+ else:
+ I += x[0] * dq
+ # verbose_print(
+ # "\tM&S: build_q_table: dn/dq ({}) = {} I -> = {}".format(q, x[0], I),
+ # verbosity,
+ # _MS_VERBOSITY_LEVEL,
+ # )
+ return I
+
+
+def fill_data(sample_values, data_dict):
+ """
+ Function that returns the normalized array of values for given logmass and logperiod
+ used for the e and q values
+
+ TODO: make sure we do the correct thing with the dstep
+ """
+
+ data = {}
+ I = 0
+
+ dstep = float(sample_values[1]) - float(sample_values[0])
+
+ # Read out the data
+ for sample_value in sample_values:
+ val = data_dict[sample_value]
+ data[sample_value] = val
+ I += val
+
+ # Normalize the data
+ for sample_value in sample_values:
+ data[sample_value] = data[sample_value] / I
+
+ return data
+
+
+def calc_e_integral(
+ options, integrals_string, interpolator_name, mass_string, period_string, verbosity=0
+):
+ """
+ Function to calculate the P integral
+
+ We need to renormalize this because min_per > 0, and not all periods should be included
+ """
+
+ global Moecache
+ min_ecc = 0
+ max_ecc = 0.9999
+
+ mass_period_string = "{}_{}".format(options[mass_string], options[period_string])
+
+ # Check if the dict exists
+ if not Moecache.get(integrals_string, None):
+ Moecache[integrals_string] = {}
+
+ # Check for cached value. If it doesn't exist: calculate
+ if not Moecache[integrals_string].get(mass_period_string, None):
+ I = 0
+ decc = 1e-3
+
+ for ecc in np.arange(min_ecc, max_ecc, decc):
+ # Loop over all the values in the table, between the min and max P
+ dp_decc = Moecache[interpolator_name].interpolate(
+ [np.log10(options[mass_string]), np.log10(options[period_string]), ecc]
+ )[0]
+
+ I += dp_decc * decc
+
+ # Set the integral value in the dict
+ Moecache[integrals_string][mass_period_string] = I
+ verbose_print(
+ "\tM&S: calc_ecc_integral: min_ecc: {} max ecc: {} integrals_string: {} interpolator_name: {} mass_string: {} period_string: {} mass: {} period: {} I: {}".format(
+ min_ecc, max_ecc, integrals_string, interpolator_name, mass_string, period_string, options[mass_string], options[period_string], I
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ else:
+ verbose_print(
+ "\tM&S: calc_ecc_integral: Found cached value for min_ecc: {} max ecc: {} integrals_string: {} interpolator_name: {} mass_string: {} period_string: {} mass: {} period: {} I: {}".format(
+ min_ecc, max_ecc, integrals_string, interpolator_name, mass_string, period_string, options[mass_string], options[period_string], Moecache[integrals_string][mass_period_string]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+
+def calc_P_integral(
+ options, min_P, integrals_string, interpolator_name, mass_string, verbosity=0
+):
+ """
+ Function to calculate the P integral
+
+ We need to renormalize this because min_per > 0, and not all periods should be included
+ """
+
+ global Moecache
+ max_logP = 10
+
+ # Check if the dict exists
+ if not Moecache.get(integrals_string, None):
+ Moecache[integrals_string] = {}
+
+ # Check for cached value. If it doesn't exist: calculate
+ if not Moecache[integrals_string].get(options[mass_string], None):
+ I = 0
+ dlogP = 1e-3
+
+ for logP in np.arange(np.log10(min_P), max_logP, dlogP):
+ # Loop over all the values in the table, between the min and max P
+ dp_dlogP = Moecache[interpolator_name].interpolate(
+ [np.log10(options[mass_string]), logP]
+ )[0]
+
+ I += dp_dlogP * dlogP
+
+ # Set the integral value in the dict
+ Moecache[integrals_string][options[mass_string]] = I
+ verbose_print(
+ "\tM&S: calc_P_integral: min_P: {} integrals_string: {} interpolator_name: {} mass_string: {} mass: {} I: {}".format(
+ min_P, integrals_string, interpolator_name, mass_string, options[mass_string], I
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ else:
+ verbose_print(
+ "\tM&S: calc_P_integral: Found cached value for min_P: {} integrals_string: {} interpolator_name: {} mass_string: {} mass: {} I: {}".format(
+ min_P, integrals_string, interpolator_name, mass_string, options[mass_string], Moecache[integrals_string][options[mass_string]]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+def calc_total_probdens(prob_dict):
+ """
+ Function to calculate the total probability density
+ """
+
+ total_probdens = 1
+ for key in prob_dict:
+ total_probdens *= prob_dict[key]
+ prob_dict['total_probdens'] = total_probdens
+
+ return prob_dict
+
+def Moe_de_Stefano_2017_pdf(options, verbosity=0):
+ """
+ Moe & distefano function to calculate the probability density.
+
+ takes a dictionary as input (in options) with options:
+
+ M1, M2, M3, M4 => masses (Msun) [M1 required, rest optional]
+ P, P2, P3 => periods (days) [number: none=binary, 2=triple, 3=quadruple]
+ ecc, ecc2, ecc3 => eccentricities [numbering as for P above]
+
+ mmin => minimum allowed stellar mass (default 0.07)
+ mmax => maximum allowed stellar mass (default 80.0)
+ """
+
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf with options:\n\t\t{}".format(
+ json.dumps(options)
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ prob_dict = {} # Dictionary containing all the pdf values for the different parameters
+
+ # Get the multiplicity from the options, and if its not there, calculate it based on the
+ # TODO: the function below makes no sense. We NEED to pass the multiplicity in the
+ if not options.get("multiplicity", None):
+ msg = "\tM&S: Moe_de_Stefano_2017_pdf: Did not find a multiplicity value in the options dictionary"
+ verbose_print(
+ msg,
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ raise ValueError(msg)
+ # multiplicity = 1
+ # for n in range(2, 5):
+ # multiplicity += 1 if options.get("M{}".format(n), None) else 0
+ else:
+ multiplicity = options["multiplicity"]
+
+ # Immediately return 0 if the multiplicity modulator is 0
+ if options["multiplicity_modulator"][multiplicity - 1] == 0:
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: returning 0 because of the multiplicity modulator being 0",
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ return 0
+
+ ############################################################
+ # multiplicity fraction
+ # Calculate the probabilty, or rather, fraction, of stars that belong to this mass
+
+ multiplicity_probability = Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity)[multiplicity - 1]
+ prob_dict['multiplicity'] = multiplicity_probability
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Appended multiplicity (mass1 = {}) probability ({}) to the prob dict ({})".format(
+ options["M1"], prob_dict['multiplicity'], prob_dict
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ ############################################################
+ # always require an IMF for the primary star
+ #
+ # NB multiply by M1 to convert dN/dM to dN/dlnM
+ # (dlnM = dM/M, so 1/dlnM = M/dM)
+
+ # TODO: Create an n-part-powerlaw method that can have breakpoints and slopes. I'm using a three-part powerlaw now.
+ # TODO: is this actually the correct way? putting the M1 in there? Do we sample in logspace?
+ M1_probability = Kroupa2001(options["M1"]) * options["M1"]
+ prob_dict['M1'] = M1_probability
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Appended Mass (m={}) probability ({}) to the prob dict ({})".format(
+ options["M1"], prob_dict['M1'], prob_dict
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if M1_probability == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ """
+ From here we go through the multiplicities.
+ """
+ if multiplicity >= 2:
+ # If the multiplicity is higher than 1, we will need to construct the following tables:
+ # - period distribution table
+ # - q distribution table
+ # - eccentricity distribution table
+
+ # Set up the interpolator for the periods
+ if not Moecache.get("rinterpolator_log10P", None):
+ Moecache["rinterpolator_log10P"] = py_rinterpolate.Rinterpolate(
+ table=Moecache["period_distributions"], # Contains the table of data
+ nparams=2, # log10M, log10P
+ ndata=2, # binary, triple
+ verbosity=verbosity - (_MS_VERBOSITY_INTERPOLATOR_LEVEL - 1),
+ )
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Created new period interpolator: {}".format(
+ Moecache["rinterpolator_log10P"]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Make a table storing Moe's data for q distributions
+ if options.get("M2", None) or options.get("M3", None) or options.get("M4", None):
+ if not Moecache.get("rinterpolator_q", None):
+ Moecache["rinterpolator_q"] = py_rinterpolate.Rinterpolate(
+ table=Moecache["q_distributions"], # Contains the table of data
+ nparams=3, # log10M, log10P, q
+ ndata=1, #
+ verbosity=verbosity - (_MS_VERBOSITY_INTERPOLATOR_LEVEL - 1),
+ )
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Created new q interpolator: {}".format(
+ Moecache["rinterpolator_q"]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Make a table storing Moe's data for q distributions, but only if the ecc is actually sampled
+ if "ecc" in options:
+ if not options['ecc']==None:
+ if not Moecache.get("rinterpolator_e", None):
+ Moecache["rinterpolator_e"] = py_rinterpolate.Rinterpolate(
+ table=Moecache["ecc_distributions"], # Contains the table of data
+ nparams=3, # log10M, log10P, e
+ ndata=1, #
+ verbosity=verbosity - (_MS_VERBOSITY_INTERPOLATOR_LEVEL - 1),
+ )
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Created new e interpolator: {}".format(
+ Moecache["rinterpolator_e"]
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ ###############
+ # Calculation for period of the binary
+
+ if options.get("M2", None):
+ # Separation of the inner binary
+ options["sep"] = calc_sep_from_period(
+ options["M1"], options["M2"], options["P"]
+ )
+ # TODO: add check for min_P with instant RLOF?
+ # TODO: Actually use the value above.
+ # Total mass inner binary:
+ options["M1+M2"] = options["M1"] + options["M2"]
+
+ # Calculate P integral or use cached value
+ calc_P_integral(
+ options, 1, "P_integrals", "rinterpolator_log10P", "M1", verbosity
+ )
+
+ # Set probabilty for P1
+ p_val = Moecache["rinterpolator_log10P"].interpolate(
+ [np.log10(options["M1"]), np.log10(options["P"])]
+ )[0]
+ p_val = p_val / Moecache["P_integrals"][options["M1"]]
+ prob_dict['P'] = p_val
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Appended period (m={}, P={}) probability ({}) to the prob list ({})".format(
+ options["M1"], options["P"], prob_dict['P'], prob_dict
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['P'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ ############################################################
+ # mass ratio (0 < q = M2/M1 < qmax)
+ #
+ # we need to construct the q table for the given M1
+ # subject to qmin = Mmin/M1
+
+ if options.get("M2", None):
+ # Build the table for q
+ primary_mass = options["M1"]
+ secondary_mass = options["M2"]
+ m_label = "M1"
+ p_label = "P"
+
+ # Construct the q table
+ build_q_table(options, m_label, p_label, verbosity=verbosity)
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Created q_table ({}) for m={} p={}".format(Moecache[
+ "rinterpolator_q_given_{}_log10{}".format(m_label, p_label)
+ ], options[m_label], options[p_label]),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Add probability for the mass ratio
+ q_prob = Moecache[
+ "rinterpolator_q_given_{}_log10{}".format(m_label, p_label)
+ ].interpolate([secondary_mass / primary_mass])[0]
+ prob_dict['q'] = q_prob
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: appended mass ratio (M={} P={} q={}) probability ({}) to the prob list ({}) ".format(
+ options["M1"],
+ options["P"],
+ options["M2"] / options["M1"],
+ prob_dict['q'],
+ prob_dict,
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['q'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ ############################################################
+ # Eccentricity
+ # TODO: ask rob if the eccentricity requires an extrapolation as well.
+
+ # Only do this if the eccentricity is sampled
+ if "ecc" in options:
+ if not options['ecc']==None:
+ # Calculate ecc integral or use cached value
+ calc_e_integral(
+ options, "ecc_integrals", "rinterpolator_e", "M1", "P", verbosity
+ )
+ mass_period_string = "{}_{}".format(options["M1"], options["P"])
+
+ # Set probabilty for ecc
+ ecc_val = Moecache["rinterpolator_e"].interpolate(
+ [np.log10(options["M1"]), np.log10(options["P"]), options["ecc"]]
+ )[0]
+ ecc_val = ecc_val / Moecache["ecc_integrals"][mass_period_string]
+ prob_dict['ecc'] = ecc_val
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Appended eccentricity (m={}, P={}, ecc={}) probability ({}) to the prob list ({})".format(
+ options["M1"], options["P"], options["ecc"], prob_dict['ecc'], prob_dict
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['ecc'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ # Calculations for when multiplicity is bigger than 3
+ # BEWARE: binary_c does not evolve these systems actually and the code below should be revised for when binary_c actually evolves triples.
+ # For that reason, I would not advise to use things with multiplicity > 3
+ if multiplicity >= 3:
+
+ ############################################################
+ # orbital period 2 =
+ # orbital period of star 3 (multiplicity==3) or
+ # the star3+star4 binary (multiplicity==4)
+ #
+ # we assume the same period distribution for star 3
+ # (or stars 3 and 4) but with a separation that is >10*a*(1+e)
+ # where 10*a*(1+e) is the maximum apastron separation of
+ # stars 1 and 2
+
+ # TODO: Is this a correct assumption?
+ max_sep = 10.0 * options["sep"] * (1.0 + options["ecc"])
+ min_P2 = calc_period_from_sep(options["M1+M2"], options["mmin"], max_sep)
+
+ if options["P2"] < min_P2:
+ # period is too short : system is not hierarchical
+ prob_dict['P2'] = 0
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: period2 is too short: {} < {}, system is not hierarchichal. Added 0 to probability list".format(
+ options["P1"], min_P2
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['P2'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ else:
+ # period is long enough that the system is hierarchical
+ # hence the separation between the outer star
+ # and inner binary
+ options["sep2"] = calc_sep_from_period(
+ options["M3"], options["M1+M2"], options["P2"]
+ )
+
+ # Check for cached value of P integral or calculate
+ calc_P_integral(
+ options,
+ min_P2,
+ "P2_integrals",
+ "rinterpolator_log10P",
+ "M1+M2",
+ verbosity,
+ )
+
+ # Add the probability
+ p_val = Moecache["rinterpolator_log10P"].interpolate(
+ [np.log10(options["M1+M2"]), np.log10(options["P2"])]
+ )[0]
+ p_val = p_val / Moecache["P2_integrals"][options["M1+M2"]]
+ prob_dict['P2'] = p_val
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Appended period2 (m1={} m2={}, P2={}) probability ({}) to the prob list ({})".format(
+ options["M1"], options["M2"], options["P2"], prob_dict['P2'], prob_dict
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['P2'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ ############################################################
+ # mass ratio 2 = q2 = M3 / (M1+M2)
+ #
+ # we need to construct the q table for the given M1
+ # subject to qmin = Mmin/(M1+M2)
+
+ # Set the variables for the masses and their names
+ primary_mass = options["M1+M2"]
+ secondary_mass = options["M3"]
+ m_label = "M1+M2"
+ p_label = "P2"
+
+ # Build q table
+ build_q_table(options, m_label, p_label, verbosity=verbosity)
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Called build_q_table",
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Add the probability
+ q2_val = Moecache[
+ "rinterpolator_q_given_{}_log10{}".format(m_label, p_label)
+ ].interpolate([secondary_mass / primary_mass])[0]
+ prob_dict['q2'] = q2_val
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: appended mass ratio (M1+M2={} M3={} P={} q={}) probability ({}) to the prob list ({}) ".format(
+ options["M1+M2"],
+ options["M3"],
+ options["P"],
+ secondary_mass / primary_mass,
+ prob_dict['q2'],
+ prob_dict,
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['q2'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ # TODO: Implement ecc2 calculation
+ if multiplicity == 4:
+ # quadruple system.
+ # TODO: Ask Rob about the strructure of the quadruple. Is this only double binary quadrupples?
+
+ ############################################################
+ # orbital period 3
+ #
+ # we assume the same period distribution for star 4
+ # as for any other stars but Pmax must be such that
+ # sep3 < sep2 * 0.2
+
+ # TODO: fix this here
+ max_sep3 = 0.2 * options["sep2"] * (1.0 + options["ecc2"])
+ max_per3 = calc_period_from_sep(
+ options["M1+M2"], options["mmin"], max_sep3
+ )
+
+ # Calculate P integral or use the cached value
+ # TODO: Make sure we use the correct period idea here.
+ calc_P_integral(
+ options,
+ min_P2,
+ "P2_integrals",
+ "rinterpolator_log10P",
+ "M1+M2",
+ verbosity,
+ )
+
+ # Set probability
+ p_val = Moecache["rinterpolator_log10P"].interpolate(
+ [np.log10(options["M1+M2"]), np.log10(options["P2"])]
+ )[0]
+ p_val = p_val / Moecache["P2_integrals"][options["M1+M2"]]
+ prob_dict['P3'] = p_val
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Appended period2 (M=4) (m1={} m2={}, P2={}) probability ({}) to the prob list ({})".format(
+ options["M1"], options["M2"], options["P2"], prob_dict['P3'], prob_dict
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['P3'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ ############################################################
+ # mass ratio 2
+ #
+ # we need to construct the q table for the given M1
+ # subject to qmin = Mmin/(M1+M2)
+ # Make a table storing Moe's data for q distributions
+
+ # Build the table for q2
+ primary_mass = options["M1+M2"]
+ secondary_mass = options["M3"]
+ m_label = "M1+M2"
+ p_label = "P2"
+
+ # Calculate new q table
+ build_q_table(options, m_label, p_label, verbosity=verbosity)
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: Created q_table ".format(),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Add the probability
+ q3_prob = Moecache[
+ "rinterpolator_q_given_{}_log10{}".format(m_label, p_label)
+ ].interpolate([secondary_mass / primary_mass])[0]
+ prob_dict['q3'] = q3_prob
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: appended mass ratio (M1+M2={} M3={} P={} q={}) probability ({}) to the prob list ({}) ".format(
+ options["M1+M2"],
+ options["M3"],
+ options["P"],
+ secondary_mass / primary_mass,
+ prob_dict['q3'],
+ prob_dict,
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ # if prob_dict['q3'] == 0: # If the probability is 0 then we don't have to calculate more
+ # calc_total_probdens(prob_dict)
+ # return prob_dict
+
+ # TODO ecc 3
+
+ # check for input of multiplicity
+ elif multiplicity not in range(1, 5):
+ msg = "\tM&S: Moe_de_Stefano_2017_pdf: Unknown multiplicity {}".format(
+ multiplicity
+ )
+ verbose_print(
+ msg,
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ raise ValueError(msg)
+
+ # Calculate total probdens:
+ prob_dict = calc_total_probdens(prob_dict)
+
+ # Some info
+ if multiplicity == 1:
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: M1={} q=N/A log10P=N/A ({}): {} -> {}\n".format(
+ options["M1"],
+ len(prob_dict),
+ str(prob_dict),
+ prob_dict['total_probdens']
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ elif multiplicity == 2:
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: M1={} q={} log10P={} ecc={} ({}): {} -> {}\n".format(
+ options["M1"],
+ options["M2"] / options["M1"] if options.get("M2", None) else "N/A",
+ np.log10(options["P"]),
+ options['ecc'] if options.get("ecc", None) else "N/A",
+ len(prob_dict),
+ str(prob_dict),
+ prob_dict['total_probdens']
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ elif multiplicity == 3:
+ verbose_print(
+ "\tM&S: Moe_de_Stefano_2017_pdf: M1={} q={} log10P={} ecc={} M3={} log10P2={} ecc2={} ({}): {} -> {}".format(
+ options["M1"],
+ options["M2"] / options["M1"] if options.get("M2", None) else "N/A",
+ np.log10(options["P"]),
+ options["ecc"] if options.get("ecc", None) else "N/A",
+ options["M3"],
+ np.log10(options["P2"]),
+ options["ecc2"] if options.get("ecc2", None) else "N/A",
+ len(prob_dict),
+ str(prob_dict),
+ prob_dict['total_probdens']
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ elif multiplicity == 4:
+ verbose_print(
+ "M&S: Moe_de_Stefano_2017_pdf: M1={} q={} log10P={} ecc={} M3={} log10P2={} ecc2={} M4={} log10P3={} ecc3={} ({}) : {} -> {}".format(
+ options["M1"],
+ options["M2"] / options["M1"] if options.get("M2", None) else "N/A",
+ np.log10(options["P"]),
+ options['ecc'] if options.get("ecc", None) else "N/A",
+ options["M3"],
+ np.log10(options["P2"]),
+ options["ecc2"] if options.get("ecc2", None) else "N/A",
+ options["M4"],
+ np.log10(options["P3"]),
+ options["ecc3"] if options.get("ecc3", None) else "N/A",
+ len(prob_dict), str(prob_dict), prob_dict['total_probdens']
+ ),
+ verbosity,
+ _MS_VERBOSITY_LEVEL,
+ )
+ return prob_dict
diff --git a/binarycpython/utils/functions.py b/binarycpython/utils/functions.py
index adad1a08ea636b3edd296ef8fc093b05839bb623..30ae3800186b6fb024a0360e172b6780a70d51c4 100644
--- a/binarycpython/utils/functions.py
+++ b/binarycpython/utils/functions.py
@@ -13,35 +13,279 @@ import os
import tempfile
import copy
import inspect
-import ast
import sys
+from io import StringIO
+from typing import Union, Any
+from collections import defaultdict
+
import h5py
import numpy as np
-from io import StringIO
-from typing import Union, Any
-from collections import defaultdict
from binarycpython import _binary_c_bindings
+import binarycpython.utils.moe_distefano_data as moe_distefano_data
+
+import py_rinterpolate
+
+########################################################
+# Unsorted
+########################################################
+def convert_bytes(size):
+ """
+ Function to return the size + a magnitude string
+ """
+
+ for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
+ if size < 1024.0:
+ return "%3.1f %s" % (size, x)
+ size /= 1024.0
+
+ return size
+
+def get_size(obj, seen=None):
+ """
+ Recursively finds size of objects
+
+ From https://github.com/bosswissam/pysize
+ """
+
+ size = sys.getsizeof(obj)
+ if seen is None:
+ seen = set()
+ obj_id = id(obj)
+ if obj_id in seen:
+ return 0
+ # Important mark as seen *before* entering recursion to gracefully handle
+ # self-referential objects
+ seen.add(obj_id)
+ if isinstance(obj, dict):
+ size += sum([get_size(v, seen) for v in obj.values()])
+ size += sum([get_size(k, seen) for k in obj.keys()])
+ elif hasattr(obj, '__dict__'):
+ size += get_size(obj.__dict__, seen)
+ elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes, bytearray)):
+ size += sum([get_size(i, seen) for i in obj])
+ return size
+
+def subtract_dicts(dict_1: dict, dict_2: dict) -> dict:
+ """
+ Function to subtract two dictionaries.
+
+ Only allows values to be either a dict or a numerical type
+
+ For the overlapping keys (key name present in both dicts):
+ When the keys are of the same type:
+ - If the types are of numerical type: subtract the value at dict 2 from dict 1.
+ - If the types are both dictionaries: call this function with the subdicts
+
+ WHen the keys are not of the same type:
+ - if the keys are all of numerical types
+
+ For the unique keys:
+ - if the key is from dict 1: adds the value to the new dict (be it numerical value or dict)
+ - If the key is from dict 2: Adds the negative of its value in case of numerical type.
+ if the type is a dict, the result of subtract_dicts({}, dict_2[key]) will be set
+
+ If the result is 0, the key will be removed from the resulting dict.
+ If that results in an empty dict, the dict will be removed too.
+
+ Args:
+ dict_1: first dictionary
+ dict_2: second dictionary
+
+ Returns:
+ Subtracted dictionary
+ """
+
+ # Set up new dict
+ new_dict = {}
+
+ # Define allowed numerical types
+ ALLOWED_NUMERICAL_TYPES = (float, int, np.float64)
+
+ #
+ keys_1 = dict_1.keys()
+ keys_2 = dict_2.keys()
+
+ # Find overlapping keys of both dicts
+ overlapping_keys = set(keys_1).intersection(set(keys_2))
+
+ # Find the keys that are unique
+ unique_to_dict_1 = set(keys_1).difference(set(keys_2))
+ unique_to_dict_2 = set(keys_2).difference(set(keys_1))
+
+ # Add the unique keys to the new dict
+ for key in unique_to_dict_1:
+ # If these items are numerical types
+ if isinstance(dict_1[key], ALLOWED_NUMERICAL_TYPES):
+ new_dict[key] = dict_1[key]
+ if new_dict[key] == 0:
+ del new_dict[key]
+
+ # Else, to be safe we should deepcopy them
+ elif isinstance(dict_1[key], dict):
+ copy_dict = copy.deepcopy(dict_1[key])
+ new_dict[key] = copy_dict
+ else:
+ msg = "Error: using unsupported type for key {}: {}".format(
+ key, type(dict_1[key])
+ )
+ print(msg)
+ raise ValueError(msg)
+
+ # Add the unique keys to the new dict
+ for key in unique_to_dict_2:
+ # If these items are numerical type, we should add the negative of the value
+ if isinstance(dict_2[key], ALLOWED_NUMERICAL_TYPES):
+ new_dict[key] = -dict_2[key]
+ if new_dict[key] == 0:
+ del new_dict[key]
+
+ # Else we should place the negative of that dictionary in the new place
+ elif isinstance(dict_2[key], dict):
+ new_dict[key] = subtract_dicts({}, dict_2[key])
+ else:
+ msg = "Error: using unsupported type for key {}: {}".format(
+ key, type(dict_2[key])
+ )
+ print(msg)
+ raise ValueError(msg)
+
+ # Go over the common keys:
+ for key in overlapping_keys:
+
+ # See whether the types are actually the same
+ if not type(dict_1[key]) is type(dict_2[key]):
+ # Exceptions:
+ if (type(dict_1[key]) in ALLOWED_NUMERICAL_TYPES) and (
+ type(dict_2[key]) in ALLOWED_NUMERICAL_TYPES
+ ):
+ # We can safely subtract the values since they are all numeric
+ new_dict[key] = dict_1[key] - dict_2[key]
+ if new_dict[key] == 0:
+ del new_dict[key]
+
+ else:
+ print(
+ "Error key: {} value: {} type: {} and key: {} value: {} type: {} are not of the same type and cannot be merged".format(
+ key,
+ dict_1[key],
+ type(dict_1[key]),
+ key,
+ dict_2[key],
+ type(dict_2[key]),
+ )
+ )
+ raise ValueError
+
+ # This is where the keys are the same
+ else:
+ # If these items are numeric types
+ if isinstance(dict_1[key], ALLOWED_NUMERICAL_TYPES):
+ new_dict[key] = dict_1[key] - dict_2[key]
+
+ # Remove entry if the value is 0
+ if new_dict[key] == 0:
+ del new_dict[key]
+
+ # Else, to be safe we should deepcopy them
+ elif isinstance(dict_1[key], dict):
+ new_dict[key] = subtract_dicts(dict_1[key], dict_2[key])
+
+ # Remove entry if it results in an empty dict
+ # TODO: write test to prevent empty dicts from showing up
+ if not new_dict[key]:
+ del new_dict[key]
+ else:
+ msg = "Error: using unsupported type for key {}: {}".format(
+ key, type(dict_2[key])
+ )
+ print(msg)
+ raise ValueError(msg)
+
+ #
+ return new_dict
+
+
+def get_moe_distefano_dataset(options):
+ """
+ Function to get the default moe and Distefano dataset or accept a userinput.
+ """
+
+ if not options.get("file", None):
+ print("Using the default Moe and de Stefano 2017 datafile")
+
+ json_data = copy.deepcopy(moe_distefano_data.moe_distefano_2017_data)
+
+ else:
+ if not os.path.isfile(options["file"]):
+ print(
+ "The provided 'file' Moe and de Stefano JSON file does not seem to exist at {}".format(
+ options["file"]
+ )
+ )
+ raise ValueError
+ if not options["file"].endswith(".json"):
+ print("Provided filename does not end with .json")
+
+ else:
+ # Read input data and Clean up the data if there are whitespaces around the keys
+ with open(options["file"], "r") as data_filehandle:
+ datafile_data = data_filehandle.read()
+ datafile_data = datafile_data.replace('" ', '"')
+ datafile_data = datafile_data.replace(' "', '"')
+ datafile_data = datafile_data.replace(' "', '"')
+ json_data = json.loads(datafile_data)
+
+ return json_data
+
+
+def imports():
+ for name, val in globals().items():
+ if isinstance(val, types.ModuleType):
+ yield val.__name__
+
+
+class catchtime(object):
+ """
+ Context manager to calculate time spent
+ """
+
+ def __enter__(self):
+ self.t = time.clock()
+ return self
+
+ def __exit__(self, type, value, traceback):
+ self.t = time.clock() - self.t
+ print("Took {}s".format(self.t))
def is_capsule(o):
+ """
+ Function to tell whether object is a capsule
+ """
+
t = type(o)
- return t.__module__ == 'builtins' and t.__name__ == 'PyCapsule'
+ return t.__module__ == "builtins" and t.__name__ == "PyCapsule"
+
class Capturing(list):
+ """
+ Context manager to capture output and store it
+ """
+
def __enter__(self):
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
return self
+
def __exit__(self, *args):
self.extend(self._stringio.getvalue().splitlines())
- del self._stringio # free up some memory
+ del self._stringio # free up some memory
sys.stdout = self._stdout
-
########################################################
# utility functions
########################################################
@@ -200,13 +444,15 @@ def create_hdf5(data_dir: str, name: str) -> None:
def return_binary_c_version_info(parsed: bool = False) -> Union[str, dict]:
"""
- Function that returns the version information of binary_c. This function calls the function _binary_c_bindings.return_version_info()
+ Function that returns the version information of binary_c. This function calls the function
+ _binary_c_bindings.return_version_info()
Args:
parsed: Boolean flag whether to parse the version_info output of binary_c. default = False
Returns:
- Either the raw string of binary_c or a parsed version of this in the form of a nested dictionary
+ Either the raw string of binary_c or a parsed version of this in the form of a nested
+ dictionary
"""
found_prev = False
@@ -252,12 +498,47 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
# Clean data and put in correct shape
splitted = version_info_string.strip().splitlines()
- cleaned = set([el.strip() for el in splitted if not el == ""])
+ cleaned = {el.strip() for el in splitted if not el == ""}
+
+ ##########################
+ # Network:
+ # Split off all the networks and parse the info.
+
+ networks = {el for el in cleaned if el.startswith("Network ")}
+ cleaned = cleaned - networks
+
+ networks_dict = {}
+ for el in networks:
+ network_dict = {}
+ split_info = el.split("Network ")[-1].strip().split("==")
+
+ network_number = int(split_info[0])
+ network_dict["network_number"] = network_number
+
+ network_info_split = split_info[1].split(" is ")
+
+ shortname = network_info_split[0].strip()
+ network_dict["shortname"] = shortname
+
+ if not network_info_split[1].strip().startswith(":"):
+ network_split_info_extra = network_info_split[1].strip().split(":")
+
+ longname = network_split_info_extra[0].strip()
+ network_dict["longname"] = longname
+
+ implementation = (
+ network_split_info_extra[1].strip().replace("implemented in", "")
+ )
+ if implementation:
+ network_dict["implemented_in"] = implementation.strip().split()
+
+ networks_dict[network_number] = copy.deepcopy(network_dict)
+ version_info_dict["networks"] = networks_dict if networks_dict else None
##########################
# Isotopes:
# Split off
- isotopes = set([el for el in cleaned if el.startswith("Isotope ")])
+ isotopes = {el for el in cleaned if el.startswith("Isotope ")}
cleaned = cleaned - isotopes
isotope_dict = {}
@@ -311,7 +592,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
##########################
# ensembles:
# Split off
- ensembles = set([el for el in cleaned if el.startswith("Ensemble")])
+ ensembles = {el for el in cleaned if el.startswith("Ensemble")}
cleaned = cleaned - ensembles
ensemble_dict = {}
@@ -324,7 +605,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
##########################
# macros:
# Split off
- macros = set([el for el in cleaned if el.startswith("macroxyz")])
+ macros = {el for el in cleaned if el.startswith("macroxyz")}
cleaned = cleaned - macros
param_type_dict = {
@@ -333,6 +614,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
"MACRO": str,
"INT": int,
"LONG_INT": int,
+ "UINT": int,
}
macros_dict = {}
@@ -343,6 +625,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
new_split = "".join(split_info[1:]).split(" is ")
param_name = new_split[0]
param_value = " is ".join(new_split[1:])
+
# Sometimes the macros have extra information behind it. Needs an update in outputting by binary_c
try:
macros_dict[param_name] = param_type_dict[param_type](param_value)
@@ -353,7 +636,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
##########################
# Elements:
# Split off:
- elements = set([el for el in cleaned if el.startswith("Element")])
+ elements = {el for el in cleaned if el.startswith("Element")}
cleaned = cleaned - elements
# Fill dict:
@@ -382,7 +665,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
##########################
# dt_limits:
# split off
- dt_limits = set([el for el in cleaned if el.startswith("DTlimit")])
+ dt_limits = {el for el in cleaned if el.startswith("DTlimit")}
cleaned = cleaned - dt_limits
# Fill dict
@@ -399,7 +682,7 @@ def parse_binary_c_version_info(version_info_string: str) -> dict:
##########################
# Nucleosynthesis sources:
# Split off
- nucsyn_sources = set([el for el in cleaned if el.startswith("Nucleosynthesis")])
+ nucsyn_sources = {el for el in cleaned if el.startswith("Nucleosynthesis")}
cleaned = cleaned - nucsyn_sources
# Fill dict
@@ -481,12 +764,13 @@ def output_lines(output: str) -> list:
if output:
return output.splitlines()
- else:
- return []
+ return []
+
def example_parse_output(output: str, selected_header: str) -> dict:
"""
- Function that parses output of binary_c. This version serves as an example and is quite detailed. Custom functions can be easier:
+ Function that parses output of binary_c. This version serves as an example and is quite
+ detailed. Custom functions can be easier:
This function works in two cases:
if the caught line contains output like 'example_header time=12.32 mass=0.94 ..'
@@ -503,7 +787,8 @@ def example_parse_output(output: str, selected_header: str) -> dict:
Args:
output: binary_c output string
- selected_header: string header of the output (the start of the line that you want to process)
+ selected_header: string header of the output (the start of the line that you want to
+ process)
Returns:
dictionary containing parameters as keys and lists for the values
@@ -593,10 +878,12 @@ def get_defaults(filter_values: bool = False) -> dict:
def get_arg_keys() -> list:
"""
- Function that return the list of possible keys to give in the arg string. This function calls get_defaults()
+ Function that return the list of possible keys to give in the arg string.
+ This function calls get_defaults()
Returns:
- list of all the parameters that binary_c accepts (and has default values for, since we call get_defaults())
+ list of all the parameters that binary_c accepts (and has default values for, since
+ we call get_defaults())
"""
return list(get_defaults().keys())
@@ -630,7 +917,8 @@ def create_arg_string(
arg_dict: dict, sort: bool = False, filter_values: bool = False
) -> str:
"""
- Function that creates the arg string for binary_c. Takes a dictionary containing the arguments and writes them to a string
+ Function that creates the arg string for binary_c. Takes a dictionary containing the arguments
+ and writes them to a string
This string is missing the 'binary_c ' at the start.
Args:
@@ -684,12 +972,15 @@ def get_help(
- TODO: consider not returning None, but return empty dict
Args:
- param_name: name of the parameter that you want info from. Will get checked whether its a valid parameter name
+ param_name: name of the parameter that you want info from. Will get checked whether its a
+ valid parameter name
print_help: (optional, default = True) whether to print out the help information
- fail_silently: (optional, default = False) Whether to print the errors raised if the parameter isn't valid
+ fail_silently: (optional, default = False) Whether to print the errors raised if the
+ parameter isn't valid
Returns:
- Dictionary containing the help info. This dictionary contains 'parameter_name', 'parameter_value_input_type', 'description', optionally 'macros'
+ Dictionary containing the help info. This dictionary contains 'parameter_name',
+ 'parameter_value_input_type', 'description', optionally 'macros'
"""
available_arg_keys = get_arg_keys()
@@ -967,23 +1258,29 @@ def make_build_text() -> str:
git_branch = version_info["miscellaneous"]["git_branch"]
build_datetime = version_info["miscellaneous"]["build"]
- info_string = "This information was obtained by the following binary_c build: \n\t**binary_c git branch**: {}\t**binary_c git revision**: {}\t**Built on**: {}".format(
+ info_string = """
+This information was obtained by the following binary_c build:
+\t**binary_c git branch**: {}\t**binary_c git revision**: {}\t**Built on**: {}
+""".format(
git_branch, git_revision, build_datetime
)
- return info_string
+ return info_string.strip()
def write_binary_c_parameter_descriptions_to_rst_file(output_file: str) -> None:
"""
- Function that calls the get_help_super() to get the help text/descriptions for all the parameters available in that build.
+ Function that calls the get_help_super() to get the help text/descriptions for all the
+ parameters available in that build.
Writes the results to a .rst file that can be included in the docs.
Tasks:
- - TODO: add the specific version git branch, git build, git commit, and binary_c version to this document
+ - TODO: add the specific version git branch, git build, git commit, and binary_c version to
+ this document
Args:
- output_file: name of the output .rst faile containing the ReStructuredText formatted output of all the binary_c parameters.
+ output_file: name of the output .rst faile containing the ReStructuredText formatted output
+ of all the binary_c parameters.
"""
# Get the whole arguments dictionary
@@ -1099,7 +1396,8 @@ def inspect_dict(
) -> dict:
"""
Function to (recursively) inspect a (nested) dictionary.
- The object that is returned is a dictionary containing the key of the input_dict, but as value it will return the type of what the value would be in the input_dict
+ The object that is returned is a dictionary containing the key of the input_dict, but as value
+ it will return the type of what the value would be in the input_dict
In this way we inspect the structure of these dictionaries, rather than the exact contents.
@@ -1109,7 +1407,8 @@ def inspect_dict(
indent: (optional, default = 0) indent of the first output
Returns:
- Dictionary that has the same structure as the input_dict, but as values it has the type(input_dict[key]) (except if the value is a dict)
+ Dictionary that has the same structure as the input_dict, but as values it has the
+ type(input_dict[key]) (except if the value is a dict)
"""
structure_dict = {}
@@ -1192,8 +1491,8 @@ def merge_dicts(dict_1: dict, dict_2: dict) -> dict:
# See whether the types are actually the same
if not type(dict_1[key]) is type(dict_2[key]):
# Exceptions:
- if (type(dict_1[key]) in [int, float]) and (
- type(dict_2[key]) in [int, float]
+ if (type(dict_1[key]) in [int, float, np.float64]) and (
+ type(dict_2[key]) in [int, float, np.float64]
):
new_dict[key] = dict_1[key] + dict_2[key]
@@ -1236,8 +1535,12 @@ def merge_dicts(dict_1: dict, dict_2: dict) -> dict:
else:
print(
- "Object types {},{} not supported".format(
- type(dict_1[key]), type(dict_2[key])
+ "Object types {}: {} ({}), {} ({}) not supported.".format(
+ key,
+ dict_1[key],
+ type(dict_1[key]),
+ dict_2[key],
+ type(dict_2[key]),
)
)
raise ValueError
@@ -1246,6 +1549,98 @@ def merge_dicts(dict_1: dict, dict_2: dict) -> dict:
return new_dict
+def update_dicts(dict_1: dict, dict_2: dict) -> dict:
+ """
+ Function to update dict_1 with values of dict_2 in a recursive way.
+
+ Behaviour:
+
+ When dict keys are only present in one of either:
+ - we just add the content to the new dict
+
+ When dict keys are present in both, we decide based on the value types how to combine them:
+ - value of dict2 will be taken
+
+ Args:
+ dict_1: first dictionary
+ dict_2: second dictionary
+
+ Returns:
+ New dictionary with Updated values
+
+ """
+
+ # Set up new dict
+ new_dict = {}
+
+ #
+ keys_1 = dict_1.keys()
+ keys_2 = dict_2.keys()
+
+ # Find overlapping keys of both dicts
+ overlapping_keys = set(keys_1).intersection(set(keys_2))
+
+ # Find the keys that are unique
+ unique_to_dict_1 = set(keys_1).difference(set(keys_2))
+ unique_to_dict_2 = set(keys_2).difference(set(keys_1))
+
+ # Add the unique keys to the new dict
+ for key in unique_to_dict_1:
+ # If these items are ints or floats, then just put them in
+ if isinstance(dict_1[key], (float, int)):
+ new_dict[key] = dict_1[key]
+ # Else, to be safe we should deepcopy them
+ else:
+ copy_dict = copy.deepcopy(dict_1[key])
+ new_dict[key] = copy_dict
+
+ for key in unique_to_dict_2:
+ # If these items are ints or floats, then just put them in
+ if isinstance(dict_2[key], (float, int)):
+ new_dict[key] = dict_2[key]
+ # Else, to be safe we should deepcopy them
+ else:
+ copy_dict = copy.deepcopy(dict_2[key])
+ new_dict[key] = copy_dict
+
+ # Go over the common keys:
+ for key in overlapping_keys:
+
+ # See whether the types are actually the same
+ if not type(dict_1[key]) is type(dict_2[key]):
+ # Exceptions:
+ if (type(dict_1[key]) in [int, float]) and (
+ type(dict_2[key]) in [int, float]
+ ):
+ new_dict[key] = dict_2[key]
+
+ else:
+ print(
+ "Error key: {} value: {} type: {} and key: {} value: {} type: {} are not of the same type and cannot be merged".format(
+ key,
+ dict_1[key],
+ type(dict_1[key]),
+ key,
+ dict_2[key],
+ type(dict_2[key]),
+ )
+ )
+ raise ValueError
+
+ # Here we check for the cases that we want to explicitly catch. Ints will be added,
+ # floats will be added, lists will be appended (though that might change) and dicts will be
+ # dealt with by calling this function again.
+ else:
+ # dicts
+ if isinstance(dict_1[key], dict) and isinstance(dict_2[key], dict):
+ new_dict[key] = update_dicts(dict_1[key], dict_2[key])
+ else:
+ new_dict[key] = dict_2[key]
+
+ #
+ return new_dict
+
+
def extract_ensemble_json_from_string(binary_c_output: str) -> dict:
"""
Function to extract the ensemble_json information from a raw binary_c output string
@@ -1257,16 +1652,21 @@ def extract_ensemble_json_from_string(binary_c_output: str) -> dict:
json dictionary with the parsed ENSEMBLE_JSON data
"""
- json = None
+ json_dict = None
try:
+ # If there is no output just return an empty dict:
+ if not binary_c_output:
+ json_dict = {}
+ return json_dict
+
ensemble_jsons_strings = [
line
for line in binary_c_output.splitlines()
if line.startswith("ENSEMBLE_JSON")
]
- json = handle_ensemble_string_to_json(
+ json_dict = handle_ensemble_string_to_json(
ensemble_jsons_strings[0][len("ENSEMBLE_JSON ") :]
)
@@ -1283,7 +1683,7 @@ def extract_ensemble_json_from_string(binary_c_output: str) -> dict:
0,
)
- return json
+ return json_dict
class binarycDecoder(json.JSONDecoder):
@@ -1339,8 +1739,9 @@ class BinaryCEncoder(json.JSONEncoder):
pass
else:
return str_repr
+
# Let the base class default method raise the TypeError
- return JSONEncoder.default(self, o)
+ return json.JSONEncoder.default(self, o)
def binaryc_json_serializer(obj: Any) -> Any:
@@ -1357,10 +1758,9 @@ def binaryc_json_serializer(obj: Any) -> Any:
Either string representation of object if the object is a function, or the object itself
"""
- if inspect.isfunction(obj):
+ if inspect.isfunction(obj) or isinstance(obj, py_rinterpolate.Rinterpolate):
return str(obj)
- else:
- return obj
+ return obj
def handle_ensemble_string_to_json(raw_output):
diff --git a/binarycpython/utils/grid.py b/binarycpython/utils/grid.py
index bc73f755beb8a7106be9b2f467a0fb8995bd6ffa..53f9f66c1c47789cf73deb04d30153758c9a6611 100644
--- a/binarycpython/utils/grid.py
+++ b/binarycpython/utils/grid.py
@@ -25,22 +25,28 @@ Tasks:
"""
import os
+import gc
import sys
import copy
import json
-import datetime
import time
import uuid
import logging
+import datetime
import argparse
-import subprocess
import importlib.util
+import multiprocessing
from typing import Union, Any
-from pathos.helpers import mp as pathos_multiprocess
+import setproctitle
+import py_rinterpolate
-from pathos.pools import _ProcessPool as Pool
-from binarycpython.utils.grid_options_defaults import grid_options_defaults_dict
+from binarycpython.utils.grid_options_defaults import (
+ grid_options_defaults_dict,
+ moe_distefano_default_options,
+ _MS_VERBOSITY_LEVEL,
+)
+
from binarycpython.utils.custom_logging_functions import (
autogen_C_logging_code,
binary_c_log_code,
@@ -48,17 +54,16 @@ from binarycpython.utils.custom_logging_functions import (
)
from binarycpython.utils.functions import (
get_defaults,
- parse_binary_c_version_info,
remove_file,
filter_arg_dict,
get_help_all,
return_binary_c_version_info,
binaryc_json_serializer,
verbose_print,
- binarycDecoder,
merge_dicts,
- BinaryCEncoder,
+ update_dicts,
extract_ensemble_json_from_string,
+ get_moe_distefano_dataset,
)
# from binarycpython.utils.hpc_functions import (
@@ -69,8 +74,14 @@ from binarycpython.utils.functions import (
# get_python_details,
# )
+from binarycpython.utils.distribution_functions import (
+ Moecache,
+ LOG_LN_CONVERTER,
+ fill_data,
+ get_max_multiplicity,
+)
+
from binarycpython import _binary_c_bindings
-import copy
class Population:
@@ -90,6 +101,10 @@ class Population:
# Setting stuff will check against the defaults to see if the input is correct.
self.defaults = get_defaults()
self.cleaned_up_defaults = self._cleanup_defaults()
+ self.available_keys = list(self.defaults.keys())
+ self.special_params = [
+ el for el in list(self.defaults.keys()) if el.endswith("%d")
+ ]
# make the input dictionary
self.bse_options = {} # bse_options is just empty.
@@ -112,7 +127,7 @@ class Population:
#
self.process_ID = 0
- # Create location to store results. Users should write to this dictionary.
+ # Create location to store results. Users should write to this dictionary.
self.grid_results = {}
# Create location where ensemble results are written to
@@ -155,7 +170,6 @@ class Population:
"""
# Select the params that end with %d
- special_params = [el for el in list(self.defaults.keys()) if el.endswith("%d")]
# Go over all the input
for key in kwargs:
@@ -174,7 +188,7 @@ class Population:
True
if (key.startswith(param[:-2]) and len(param[:-2]) < len(key))
else False
- for param in special_params
+ for param in self.special_params
]
):
verbose_print(
@@ -210,11 +224,12 @@ class Population:
Function to handle settings values via the command line.
Best to be called after all the .set(..) lines, and just before the .evolve() is called
- If you input any known parameter (i.e. contained in grid_options, defaults/bse_options or custom_options),
- this function will attempt to convert the input from string (because everything is string) to the type of
- the value that option had before.
+ If you input any known parameter (i.e. contained in grid_options, defaults/bse_options
+ or custom_options), this function will attempt to convert the input from string
+ (because everything is string) to the type of the value that option had before.
- The values of the bse_options are initially all strings, but after user input they can change to ints.
+ The values of the bse_options are initially all strings, but after user input they
+ can change to ints.
The value of any new parameter (which will go to custom_options) will be a string.
@@ -281,7 +296,7 @@ class Population:
value = type(old_value)(value)
verbose_print("Success!", self.grid_options["verbosity"], 1)
- except ValueError as e:
+ except ValueError:
verbose_print(
"Tried to convert the given parameter {}/value {} to its correct type {} (from old value {}). But that wasn't possible.".format(
parameter, value, type(old_value), old_value
@@ -311,6 +326,22 @@ class Population:
argline = argline.strip()
return argline
+ def last_grid_variable(self):
+ """
+ Functon that returns the last grid variable
+ (i.e. the one with the highest grid_variable_number)
+ """
+
+ number = len(self.grid_options["_grid_variables"])
+ for grid_variable in self.grid_options["_grid_variables"]:
+ if (
+ self.grid_options["_grid_variables"][grid_variable][
+ "grid_variable_number"
+ ]
+ == number - 1
+ ):
+ return grid_variable
+
def add_grid_variable(
self,
name: str,
@@ -319,8 +350,10 @@ class Population:
resolution: str,
spacingfunc: str,
probdist: str,
- dphasevol: str,
+ dphasevol: Union[str, int],
parameter_name: str,
+ gridtype: str = "edge",
+ branchpoint: int = 0,
precode: Union[str, None] = None,
condition: Union[str, None] = None,
) -> None:
@@ -332,39 +365,58 @@ class Population:
The execution of the grid generation will be through a nested forloop.
Each of the grid variables will get create a deeper for loop.
- The real function that generates the numbers will get written to a new file in the TMP_DIR, and then loaded imported and evaluated.
- beware that if you insert some destructive piece of code, it will be executed anyway. Use at own risk.
+ The real function that generates the numbers will get written to a new file in the TMP_DIR,
+ and then loaded imported and evaluated.
+ beware that if you insert some destructive piece of code, it will be executed anyway.
+ Use at own risk.
Args:
name:
- name of parameter. This is evaluated as a parameter and you can use it throughout the rest of the function
+ name of parameter. This is evaluated as a parameter and you can use it throughout
+ the rest of the function
example: name = 'lnm1'
longname:
Long name of parameter
example: longname = 'Primary mass'
range:
- Range of values to take. Does not get used really, the spacingfunction is used to get the values from
+ Range of values to take. Does not get used really, the spacingfunction is used to
+ get the values from
example: range = [math.log(m_min), math.log(m_max)]
resolution:
- Resolution of the sampled range (amount of samples). TODO: check if this is used anywhere
+ Resolution of the sampled range (amount of samples).
+ TODO: check if this is used anywhere
example: resolution = resolution["M_1"]
spacingfunction:
- Function determining how the range is sampled. You can either use a real function, or a string representation of a function call. Will get written to a file and then evaluated.
- example: spacingfunction = "const(math.log(m_min), math.log(m_max), {})".format(resolution['M_1'])
+ Function determining how the range is sampled. You can either use a real function,
+ or a string representation of a function call. Will get written to a file and
+ then evaluated.
+ example:
+ spacingfunction = "const(math.log(m_min), math.log(m_max), {})".format(
+ resolution['M_1']
+ )
precode:
- Extra room for some code. This code will be evaluated within the loop of the sampling function (i.e. a value for lnm1 is chosen already)
+ Extra room for some code. This code will be evaluated within the loop of the
+ sampling function (i.e. a value for lnm1 is chosen already)
example: precode = 'M_1=math.exp(lnm1);'
probdist:
FUnction determining the probability that gets asigned to the sampled parameter
example: probdist = 'Kroupa2001(M_1)*M_1'
dphasevol:
- part of the parameter space that the total probability is calculated with
+ part of the parameter space that the total probability is calculated with. Put to -1
+ if you want to ignore any dphasevol calculations and set the value to 1
example: dphasevol = 'dlnm1'
condition:
condition that has to be met in order for the grid generation to continue
example: condition = 'self.grid_options['binary']==1'
+ gridtype:
+ Method on how the value range is sampled. Can be either 'edge' (steps starting at
+ the lower edge of the value range) or 'center'
+ (steps starting at lower edge + 0.5 * stepsize).
"""
+ # TODO: Add check for the gridtype input value
+ # TODO: add functionality for branchpoint
+
# Add grid_variable
grid_variable = {
"name": name,
@@ -377,6 +429,8 @@ class Population:
"dphasevol": dphasevol,
"parameter_name": parameter_name,
"condition": condition,
+ "gridtype": gridtype,
+ "branchpoint": branchpoint,
"grid_variable_number": len(self.grid_options["_grid_variables"]),
}
@@ -437,10 +491,17 @@ class Population:
Function that returns all the information about the population and binary_c
Args:
- include_population_settings: whether to include the population_settings (see function return_population_settings)
- include_binary_c_defaults: whether to include a dict containing the binary_c parameters and their default values
- include_binary_c_version_info: whether to include a dict containing all the binary_c version info (see return_binary_c_version_info)
- include_binary_c_help_all: whether to include a dict containing all the information about the binary_c parameters (see get_help_all)
+ include_population_settings:
+ whether to include the population_settings (see function return_population_settings)
+ include_binary_c_defaults:
+ whether to include a dict containing the binary_c parameters and their default
+ values
+ include_binary_c_version_info:
+ whether to include a dict containing all the binary_c version info
+ (see return_binary_c_version_info)
+ include_binary_c_help_all:
+ whether to include a dict containing all the information about
+ the binary_c parameters (see get_help_all)
Return:
dictionary containing all, or part of, the above dictionaries
@@ -586,7 +647,9 @@ class Population:
self.grid_options["custom_logging_func_memaddr"],
self.grid_options["_custom_logging_shared_library_file"],
) = create_and_load_logging_function(
- custom_logging_code, verbose=self.grid_options["verbosity"], custom_tmp_dir=self.grid_options["tmp_dir"]
+ custom_logging_code,
+ verbose=self.grid_options["verbosity"],
+ custom_tmp_dir=self.grid_options["tmp_dir"],
)
elif self.grid_options["C_auto_logging"]:
@@ -606,7 +669,9 @@ class Population:
self.grid_options["custom_logging_func_memaddr"],
self.grid_options["_custom_logging_shared_library_file"],
) = create_and_load_logging_function(
- custom_logging_code, verbose=self.grid_options["verbosity"], custom_tmp_dir=self.grid_options["tmp_dir"]
+ custom_logging_code,
+ verbose=self.grid_options["verbosity"],
+ custom_tmp_dir=self.grid_options["tmp_dir"],
)
###################################################
@@ -640,6 +705,9 @@ class Population:
If neither of the above is set, we continue without using HPC routines
(that doesn't mean this cannot be run on a server with many cores)
+
+ Returns an dictionary containing the analytics of the run
+ TODO: change the way this is done. Slurm & CONDOR should probably do this different
"""
# Just to make sure we don't have stuff from a previous run hanging around
@@ -672,10 +740,16 @@ class Population:
"total_count": self.grid_options["_count"],
"start_timestamp": self.grid_options["_start_time_evolution"],
"end_timestamp": self.grid_options["_end_time_evolution"],
+ "total_mass_run": self.grid_options["_total_mass_run"],
+ "total_probability_weighted_mass_run": self.grid_options[
+ "_total_probability_weighted_mass_run"
+ ],
+ "zero_prob_stars_skipped": self.grid_options['_zero_prob_stars_skipped']
}
##
- # Clean up code: remove files, unset values. This is placed in the general evolve function, because that makes for easier control
+ # Clean up code: remove files, unset values, unload interpolators etc. This is placed in the general evolve function,
+ # because that makes for easier control
self._cleanup()
return analytics_dict
@@ -771,6 +845,70 @@ class Population:
0,
)
+ def get_stream_logger(self, level=logging.DEBUG):
+ """Return logger with configured StreamHandler."""
+ stream_logger = logging.getLogger("stream_logger")
+ stream_logger.handlers = []
+ stream_logger.setLevel(level)
+ sh = logging.StreamHandler()
+ sh.setLevel(level)
+ fmt = "[%(asctime)s %(levelname)-8s %(processName)s] --- %(message)s"
+ formatter = logging.Formatter(fmt)
+ sh.setFormatter(formatter)
+ stream_logger.addHandler(sh)
+
+ return stream_logger
+
+ def system_queue_filler(self, job_queue, amt_cores):
+ """
+ Function that is responsible for keeping the queue filled.
+
+ This will generate the systems until it is full, and then keeps trying to fill it.
+ Will have to play with the size of this.
+ """
+ stream_logger = self.get_stream_logger()
+ stream_logger.debug(f"setting up the system_queue_filler now")
+
+ # Setup of the generator
+ self._generate_grid_code(dry_run=False)
+
+ self._load_grid_function()
+
+ generator = self.grid_options["_system_generator"](self, print_results=False)
+
+ # TODO: build in method to handle with the HPC.
+ # Continously fill the queue
+ for system_number, system_dict in enumerate(generator):
+ # Put job in queue
+ job_queue.put((system_number, system_dict))
+
+ # Print some info
+ # stream_logger.debug(f"producing: {system_number}") # DEBUG
+ verbose_print(
+ "Queue produced system {}".format(system_number),
+ self.grid_options["verbosity"],
+ 2,
+ )
+
+ # Print current size
+ # print("Current size: {}".format(save_que.qsize()))
+
+ # if system_number%10==0:
+ # print("system_queue_filler: system_number: {}".format(system_number))
+ # bytes_size_Moecache = get_size(Moecache)
+ # print("\tsystem_queue_filler: Size moecache: {}".format(convert_bytes(bytes_size_Moecache)))
+
+ # bytes_size_distribution_constants = get_size(distribution_constants)
+ # print("\tsystem_queue_filler: Size distribution_constants: {}".format(convert_bytes(bytes_size_distribution_constants)))
+
+ # bytes_size_self = get_size(dir(self))
+ # print("\tsystem_queue_filler: Size dir(self): {}".format(convert_bytes(bytes_size_self)))
+
+ # Send closing signal to workers. When they receive this they will terminate
+ stream_logger.debug(f"Signaling stop to processes") # DEBUG
+ for _ in range(amt_cores):
+ job_queue.put("STOP")
+
def _evolve_population_grid(self):
"""
Function to evolve the population with multiprocessing approach.
@@ -806,30 +944,57 @@ class Population:
# https://www.programcreek.com/python/example/58176/multiprocessing.Value
# https://stackoverflow.com/questions/17377426/shared-variable-in-pythons-multiprocessing
- # Create the pool
- pool = Pool(processes=self.grid_options["amt_cores"])
-
- # start the processes by giving them an ID value
- result = list(
- pool.imap_unordered(
- self._process_run_population_grid, range(self.grid_options["amt_cores"])
+ # Set process name
+ setproctitle.setproctitle('binarycpython parent process')
+ setproctitle.setthreadtitle("binarycpyhon parent thread")
+
+ # Set up the manager object that can share info between processes
+ manager = multiprocessing.Manager()
+ job_queue = manager.Queue(maxsize=self.grid_options["max_queue_size"])
+ result_queue = manager.Queue(maxsize=self.grid_options["amt_cores"])
+
+ # Create process instances
+ processes = []
+ for ID in range(self.grid_options["amt_cores"]):
+ processes.append(
+ multiprocessing.Process(
+ target=self._process_run_population_grid,
+ args=(job_queue, result_queue, ID),
+ )
)
- )
- # Handle clean termination of the whole multiprocessing (making sure there are no zombie
- # processes (https://en.wikipedia.org/wiki/Zombie_process))
- pool.close()
- pool.join()
+ # Activate the processes
+ for p in processes:
+ p.start()
+
+ # Set up the system_queue
+ self.system_queue_filler(job_queue, amt_cores=self.grid_options["amt_cores"])
+
+ # Join the processes
+ for p in processes:
+ p.join()
# Handle the results by merging all the dictionaries. How that merging happens exactly is
# described in the merge_dicts description.
combined_output_dict = {}
- for output_dict in result:
+
+ sentinel = object()
+ for output_dict in iter(result_queue.get, sentinel):
combined_output_dict = merge_dicts(combined_output_dict, output_dict)
+ if result_queue.empty():
+ break
# Put the values back as object properties
self.grid_results = combined_output_dict["results"]
- self.grid_ensemble_results = combined_output_dict['ensemble_results'] # Ensemble results are also passed as output from that dictionary
+ self.grid_ensemble_results = combined_output_dict[
+ "ensemble_results"
+ ] # Ensemble results are also passed as output from that dictionary
+
+ # Add some metadata
+ self.grid_ensemble_results["population_id"] = self.grid_options[
+ "_population_id"
+ ]
+
self.grid_options["_failed_count"] = combined_output_dict["_failed_count"]
self.grid_options["_failed_prob"] = combined_output_dict["_failed_prob"]
self.grid_options["_failed_systems_error_codes"] = list(
@@ -839,6 +1004,11 @@ class Population:
self.grid_options["_errors_found"] = combined_output_dict["_errors_found"]
self.grid_options["_probtot"] = combined_output_dict["_probtot"]
self.grid_options["_count"] = combined_output_dict["_count"]
+ self.grid_options["_total_mass_run"] = combined_output_dict["_total_mass_run"]
+ self.grid_options[
+ "_total_probability_weighted_mass_run"
+ ] = combined_output_dict["_total_probability_weighted_mass_run"]
+ self.grid_options['_zero_prob_stars_skipped'] = combined_output_dict['_zero_prob_stars_skipped']
def _evolve_system_mp(self, full_system_dict):
"""
@@ -872,28 +1042,57 @@ class Population:
if self.grid_options["parse_function"]:
self.grid_options["parse_function"](self, out)
- def _process_run_population_grid(self, ID):
+ def _process_run_population_grid(self, job_queue, result_queue, ID):
"""
- Function that loops over the whole generator, but only runs
- systems that fit to: if (localcounter+ID) % self.grid_options["amt_cores"] == 0
+ Worker process that gets items from the job_queue and runs those systems.
+ It keeps track of several things like failed systems, total time spent on systems etc.
- That way with e.g. 4 processes, process 1 runs sytem 0, 4, 8... process 2 runs system 1, 5, 9..., etc
+ Input:
+ job_queue: Queue object containing system dicts
+ result_queue: Queue where the resulting analytic dictionaries will be put in
+ ID: id of the worker process
- This function is called by _evolve_population_grid
"""
# set start timer
start_process_time = datetime.datetime.now()
+ #
self.process_ID = (
ID # Store the ID as a object property again, lets see if that works.
)
+ stream_logger = self.get_stream_logger()
+ stream_logger.debug(f"Setting up processor: process-{self.process_ID}")
+
+ # Set the process names
+ name = 'binarycpython population thread {}'.format(ID)
+ name_proc = 'binarycpython population process {}'.format(ID)
+ setproctitle.setproctitle(name_proc)
+ setproctitle.setthreadtitle(name)
+
+ # Set to starting up
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "process_status",
+ "process_{}.txt".format(self.process_ID),
+ ),
+ "w",
+ ) as f:
+ f.write("STARTING")
+
# lets try out making stores for all the grids:
self.grid_options["_store_memaddr"] = _binary_c_bindings.return_store_memaddr()
verbose_print(
- "Process {} started at {}.\tUsing store memaddr {}".format(ID, datetime.datetime.now().isoformat(), self.grid_options["_store_memaddr"]), self.grid_options["verbosity"], 0
+ "Process {} started at {}.\tUsing store memaddr {}".format(
+ ID,
+ datetime.datetime.now().isoformat(),
+ self.grid_options["_store_memaddr"],
+ ),
+ self.grid_options["verbosity"],
+ 0,
)
# Set the ensemble memaddr
@@ -908,17 +1107,12 @@ class Population:
}
verbose_print(
- "\tUsing persistent_data memaddr: {}".format(persistent_data_memaddr), self.grid_options["verbosity"], 0
+ "\tUsing persistent_data memaddr: {}".format(persistent_data_memaddr),
+ self.grid_options["verbosity"],
+ 0,
)
- # apparently we have to re-load this for every process, otherwise NameErrors arise (seems like a bug but I'm not sure)
- self._load_grid_function()
-
- # Set up generator
- generator = self.grid_options["_system_generator"](self, print_results=False)
-
# Set up local variables
- running = True
localcounter = (
0 # global counter for the whole loop. (need to be ticked every loop)
)
@@ -928,75 +1122,172 @@ class Population:
number_of_systems_run = (
0 # counter for the actual amt of systems this thread ran
)
-
- round_number_mod = 0 # rotating modulo
+ zero_prob_stars_skipped = 0
total_time_calling_binary_c = 0
- # Go over the generator
- while running:
- # round_number_mod = (localcounter+1)%self.grid_options["amt_cores"]
+ total_mass_run = 0
+ total_probability_weighted_mass_run = 0
- try:
- # Get the system
- system = next(generator)
+ # Go over the queue
+ for system_number, system_dict in iter(job_queue.get, "STOP"):
+ if localcounter == 0:
- # Check if the ID is the correct one for this process
- if (localcounter + (ID + round_number_mod)) % self.grid_options["amt_cores"] == 0:
+ # Set status to running
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "process_status",
+ "process_{}.txt".format(self.process_ID),
+ ),
+ "w",
+ ) as f:
+ f.write("RUNNING")
+
+ # if system_number%10==0:
+ # print("_process_run_population_grid: system_number: {}".format(system_number))
+ # bytes_size_Moecache = get_size(Moecache)
+ # print("\t_process_run_population_grid: Size moecache: {}".format(convert_bytes(bytes_size_Moecache)))
+
+ # bytes_size_distribution_constants = get_size(distribution_constants)
+ # print("\t_process_run_population_grid: Size distribution_constants: {}".format(convert_bytes(bytes_size_distribution_constants)))
+
+ # bytes_size_self = get_size(dir(self))
+ # print("\t_process_run_population_grid: Size dir(self): {}".format(convert_bytes(bytes_size_self)))
+
+
+ # Combine that with the other settings
+ full_system_dict = self.bse_options.copy()
+ full_system_dict.update(system_dict)
+
+ # In the first system, explicitly check all the keys that are passed to see if
+ # they match the keys known to binary_c.
+ # Won't do that every system cause that is a bit of a waste of computing time.
+ if number_of_systems_run == 0:
+ # TODO: Put this someplace else and wrap in a functioncall
+ for key in full_system_dict.keys():
+ if not key in self.available_keys:
+ # Deal with special keys
+ if not any(
+ [
+ True
+ if (
+ key.startswith(param[:-2])
+ and len(param[:-2]) < len(key)
+ )
+ else False
+ for param in self.special_params
+ ]
+ ):
+ msg = "Error: Found a parameter unknown to binary_c: {}. Abort".format(
+ key
+ )
+ raise ValueError(msg)
+
+ # self._print_info(
+ # i + 1, self.grid_options["_total_starcount"], full_system_dict
+ # )
- # Combine that with the other settings
- full_system_dict = self.bse_options.copy()
- full_system_dict.update(system)
+ #
+ verbose_print(
+ "Process {} is handling system {}".format(ID, system_number),
+ self.grid_options["verbosity"],
+ 2,
+ )
- # self._print_info(
- # i + 1, self.grid_options["_total_starcount"], full_system_dict
- # )
+ # In some cases, the whole run crashes. To be able to figure out which system
+ # that was on, we log each current system to a file (each thread has one).
+ # Each new system overrides the previous
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "current_system",
+ "process_{}.txt".format(self.process_ID),
+ ),
+ "w",
+ ) as f:
+ binary_cmdline_string = self._return_argline(full_system_dict)
+ f.write(binary_cmdline_string)
- #
- verbose_print(
- "Process {} is handling system {}".format(ID, localcounter), self.grid_options["verbosity"], 2
- )
+ start_runtime_binary_c = time.time()
- # In some cases, the whole run crashes. To be able to figure out which system that was on, we log each current system to a file (each thread has one). Each new system overrides the previous
- with open(os.path.join(self.grid_options["tmp_dir"], "thread_{}_current_system.txt".format(self.process_ID)), 'w') as f:
- binary_cmdline_string = self._return_argline(full_system_dict)
- f.write(binary_cmdline_string)
+ # If we want to actually evolve the systems
+ if self.grid_options["_actually_evolve_system"]:
+ run_system = True
- start_runtime_binary_c = time.time()
+ # Check option to ignore 0 probability systems
+ if not self.grid_options["run_zero_probability_system"]:
+ if full_system_dict['probability'] == 0:
+ run_system = False
+ zero_prob_stars_skipped += 1
+ if run_system:
# Evolve the system
self._evolve_system_mp(full_system_dict)
- end_runtime_binary_c = time.time()
-
- total_time_calling_binary_c += end_runtime_binary_c - start_runtime_binary_c # keep track of total binary_c call time
-
- # Debug line: logging all the lines
- if self.grid_options['log_runtime_systems']==1:
- with open(os.path.join(self.grid_options["tmp_dir"], "thread_{}_runtime_systems.txt".format(self.process_ID)), 'a+') as f:
- binary_cmdline_string = self._return_argline(full_system_dict)
- f.write("{} {}\n".format(end_runtime_binary_c-start_runtime_binary_c, binary_cmdline_string))
+ end_runtime_binary_c = time.time()
- # Keep track of systems:
- probability_of_systems_run += full_system_dict["probability"]
- number_of_systems_run += 1
+ total_time_calling_binary_c += (
+ end_runtime_binary_c - start_runtime_binary_c
+ ) # keep track of total binary_c call time
- except StopIteration:
- running = False
+ # Debug line: logging all the lines
+ if self.grid_options["log_runtime_systems"] == 1:
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "runtime_systems",
+ "process_{}.txt".format(self.process_ID),
+ ),
+ "a+",
+ ) as f:
+ binary_cmdline_string = self._return_argline(full_system_dict)
+ f.write(
+ "{} {} '{}'\n".format(
+ start_runtime_binary_c,
+ end_runtime_binary_c - start_runtime_binary_c,
+ binary_cmdline_string,
+ )
+ )
- if (localcounter+1)%self.grid_options["amt_cores"]==0:
- round_number_mod += 1
+ # Keep track of systems:
+ probability_of_systems_run += full_system_dict["probability"]
+ number_of_systems_run += 1
+ localcounter += 1
- # print("thread {} round_nr_mod {}. localcounter {}".format(ID, round_number_mod, localcounter))
+ # Tally up some numbers
+ total_mass_system = (
+ full_system_dict.get("M_1", 0)
+ + full_system_dict.get("M_2", 0)
+ + full_system_dict.get("M_3", 0)
+ + full_system_dict.get("M_4", 0)
+ )
+ total_mass_run += total_mass_system
+ total_probability_weighted_mass_run += (
+ total_mass_system * full_system_dict["probability"]
+ )
- # Has to be here because this one is used for the (localcounter+ID) % (self..)
- localcounter += 1
+ # Set status to finishing
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "process_status",
+ "process_{}.txt".format(self.process_ID),
+ ),
+ "w",
+ ) as f:
+ f.write("FINISHING")
+ stream_logger.debug(f"Process-{self.process_ID} is finishing.")
# Handle ensemble output: is ensemble==1, then either directly write that data to a file, or combine everything into 1 file.
ensemble_json = {} # Make sure it exists already
if self.bse_options.get("ensemble", 0) == 1:
verbose_print(
- "Process {}: is freeing ensemble output (using persisten_data memaddr {})".format(ID, self.persistent_data_memory_dict[self.process_ID]), self.grid_options["verbosity"], 2
+ "Process {}: is freeing ensemble output (using persisten_data memaddr {})".format(
+ ID, self.persistent_data_memory_dict[self.process_ID]
+ ),
+ self.grid_options["verbosity"],
+ 2,
)
ensemble_raw_output = (
@@ -1004,23 +1295,28 @@ class Population:
self.persistent_data_memory_dict[self.process_ID]
)
)
- if ensemble_raw_output == None:
+ if ensemble_raw_output is None:
verbose_print(
- "Process {}: Warning! Ensemble output is empty. ".format(ID), self.grid_options["verbosity"], 2
+ "Process {}: Warning! Ensemble output is empty. ".format(ID),
+ self.grid_options["verbosity"],
+ 1,
)
#
- if self.grid_options["combine_ensemble_with_thread_joining"] == True:
+ if self.grid_options["combine_ensemble_with_thread_joining"] is True:
verbose_print(
- "Process {}: Extracting ensemble info from raw string".format(ID), self.grid_options["verbosity"], 2
+ "Process {}: Extracting ensemble info from raw string".format(ID),
+ self.grid_options["verbosity"],
+ 2,
)
- ensemble_json['ensemble'] = extract_ensemble_json_from_string(
+ ensemble_json["ensemble"] = extract_ensemble_json_from_string(
ensemble_raw_output
) # Load this into a dict so that we can combine it later
else:
- # If we do not allow this, automatically we will export this to the data_dir, in some formatted way
+ # If we do not allow this, automatically we will export this to the data_dir, in
+ # some formatted way
output_file = os.path.join(
self.custom_options["data_dir"],
"ensemble_output_{}_{}.json".format(
@@ -1041,7 +1337,6 @@ class Population:
# free store mem:
_binary_c_bindings.free_store_memaddr(self.grid_options["_store_memaddr"])
-
# Return a set of results and errors
output_dict = {
"results": self.grid_results,
@@ -1055,27 +1350,82 @@ class Population:
"_errors_found": self.grid_options["_errors_found"],
"_probtot": probability_of_systems_run,
"_count": number_of_systems_run,
+ "_total_mass_run": total_mass_run,
+ "_total_probability_weighted_mass_run": total_probability_weighted_mass_run,
+ "_zero_prob_stars_skipped": zero_prob_stars_skipped,
}
end_process_time = datetime.datetime.now()
verbose_print(
- "Process {} finished:\n\tgenerator started at {}, done at {} (total: {}s of which {}s interfacing with binary_c).\n\tRan {} systems with a total probability of {}.\n\tThis thread had {} failing systems with a total probability of {}".format(
+ "Process {} finished:\n\tgenerator started at {}, done at {} (total: {}s of which {}s interfacing with binary_c).\n\tRan {} systems with a total probability of {}.\n\tThis thread had {} failing systems with a total probability of {}.\n\tSkipped a total of {} systems because they had 0 probability".format(
ID,
start_process_time.isoformat(),
end_process_time.isoformat(),
- (end_process_time-start_process_time).total_seconds(),
+ (end_process_time - start_process_time).total_seconds(),
total_time_calling_binary_c,
number_of_systems_run,
probability_of_systems_run,
self.grid_options["_failed_count"],
self.grid_options["_failed_prob"],
+ zero_prob_stars_skipped,
+
),
self.grid_options["verbosity"],
0,
)
- return output_dict
+ # Write summary
+ summary_dict = {
+ "population_id": self.grid_options["_population_id"],
+ "process_id": self.process_ID,
+ "start_process_time": start_process_time.timestamp(),
+ "end_process_time": end_process_time.timestamp(),
+ "total_time_calling_binary_c": total_time_calling_binary_c,
+ "number_of_systems_run": number_of_systems_run,
+ "probability_of_systems_run": probability_of_systems_run,
+ "failed_systems": self.grid_options["_failed_count"],
+ "failed_probability": self.grid_options["_failed_prob"],
+ "failed_system_error_codes": self.grid_options[
+ "_failed_systems_error_codes"
+ ],
+ "zero_prob_stars_skipped": zero_prob_stars_skipped,
+ }
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "process_summary",
+ "process_{}.json".format(self.process_ID),
+ ),
+ "w",
+ ) as f:
+ f.write(json.dumps(summary_dict, indent=4))
+
+ # Set status to running
+ with open(
+ os.path.join(
+ self.grid_options["tmp_dir"],
+ "process_status",
+ "process_{}.txt".format(self.process_ID),
+ ),
+ "w",
+ ) as f:
+ f.write("FINISHED")
+
+ result_queue.put(output_dict)
+ stream_logger.debug(f"Process-{self.process_ID} is finished.")
+
+ # Clean up the interpolators if they exist
+ # TODO: make a cleanup function for the individual threads
+ # TODO: make sure this is necessary. Actually its probably not, because we have a centralized queue
+ verbose_print(
+ "Process {}: Cleaning up interpolators".format(ID),
+ self.grid_options["verbosity"],
+ 1,
+ )
+ self._clean_interpolators()
+
+ return
# Single system
def evolve_single(self, clean_up_custom_logging_files: bool = True) -> Any:
@@ -1135,6 +1485,24 @@ class Population:
function
"""
+ # Make sure the subdirs of the tmp dir exist:
+ os.makedirs(
+ os.path.join(self.grid_options["tmp_dir"], "failed_systems"), exist_ok=True
+ )
+ os.makedirs(
+ os.path.join(self.grid_options["tmp_dir"], "current_system"), exist_ok=True
+ )
+ os.makedirs(
+ os.path.join(self.grid_options["tmp_dir"], "process_status"), exist_ok=True
+ )
+ os.makedirs(
+ os.path.join(self.grid_options["tmp_dir"], "process_summary"), exist_ok=True
+ )
+ os.makedirs(
+ os.path.join(self.grid_options["tmp_dir"], "runtime_systems"), exist_ok=True
+ )
+
+ # Check for parse function
if not self.grid_options["parse_function"]:
print("Warning: No parse function set. Make sure you intended to do this.")
@@ -1215,11 +1583,14 @@ class Population:
"_start_time_evolution"
] = time.time() # Setting start time of grid
- #
- self._generate_grid_code(dry_run=False)
+ # # Making sure the loaded grid code isn't lingering in the main PID
+ # self._generate_grid_code(dry_run=False)
+
+ # #
+ # self._load_grid_function()
#
- self._load_grid_function()
+ self.grid_options["_system_generator"] = None
# Source file
elif self.grid_options["evolution_type"] == "source_file":
@@ -1281,6 +1652,8 @@ class Population:
self.grid_options["_errors_found"] = False
self.grid_options["_errors_exceeded"] = False
self.grid_options["_failed_systems_error_codes"] = []
+ self.grid_options["_total_mass_run"] = 0
+ self.grid_options["_total_probability_weighted_mass_run"] = 0
# Remove files
# TODO: remove files
@@ -1291,6 +1664,14 @@ class Population:
# Unload/free custom_logging_code
# TODO: cleanup custom logging code.
+ # Clean up the interpolator functions
+ self._clean_interpolators()
+
+ # Also remove the rest of the contents
+ keys_moecache = list(Moecache.keys())
+ for key in keys_moecache:
+ del Moecache[key]
+
###################################################
# Gridcode functions
#
@@ -1314,6 +1695,9 @@ class Population:
# TODO: add sensible description to this function.
# TODO: Check whether all the probability and phasevol values are correct.
# TODO: import only the necessary packages/functions
+ # TODO: Put all the masses, eccentricities and periods in there already
+ # TODO: Put the certain blocks that are repeated in some subfunctions
+ # TODO: make sure running systems with multicplity 3+ is also possible.
Results in a generated file that contains a system_generator function.
"""
@@ -1373,6 +1757,21 @@ class Population:
code_string += indent * depth + "phasevol = 1\n"
code_string += indent * depth + "\n"
+ # Setting up the system parameters
+ code_string += indent * depth + "M_1 = None\n"
+ code_string += indent * depth + "M_2 = None\n"
+ code_string += indent * depth + "M_3 = None\n"
+ code_string += indent * depth + "M_4 = None\n"
+
+ code_string += indent * depth + "orbital_period = None\n"
+ code_string += indent * depth + "orbital_period_triple = None\n"
+ code_string += indent * depth + "orbital_period_quadruple = None\n"
+
+ code_string += indent * depth + "eccentricity = None\n"
+ code_string += indent * depth + "eccentricity2 = None\n"
+ code_string += indent * depth + "eccentricity3 = None\n"
+ code_string += indent * depth + "\n"
+
# Prepare the probability
code_string += indent * depth + "# setting probability lists\n"
for grid_variable_el in sorted(
@@ -1400,38 +1799,6 @@ class Population:
print("Constructing/adding: {}".format(grid_variable_el[0]))
grid_variable = grid_variable_el[1]
- #################################################################################
- # Check condition and generate forloop
-
- # If the grid variable has a condition, write the check and the action
- if grid_variable["condition"]:
- # Add comment
- code_string += (
- indent * depth
- + "# Condition for {}".format(grid_variable["parameter_name"])
- + "\n"
- )
-
- # Add condition check
- code_string += (
- indent * depth
- + "if not {}:".format(grid_variable["condition"])
- + "\n"
- )
-
- # Add condition failed action:
- code_string += (
- indent * (depth + 1)
- + 'print("Condition for {} not met!")'.format(
- grid_variable["parameter_name"]
- )
- + "\n"
- )
- code_string += indent * (depth + 1) + "raise ValueError" + "\n"
-
- # Add some whiteline
- code_string += indent * (depth + 1) + "\n"
-
#########################
# Setting up the forloop
# Add comment for forloop
@@ -1452,13 +1819,23 @@ class Population:
# TODO: Make clear that the phasevol only works good
# TODO: add option to ignore this phasevol calculation and set it to 1
# if you sample linearly in that thing.
- code_string += (
- indent * depth
- + "phasevol_{} = sampled_values_{}[1]-sampled_values_{}[0]".format(
- grid_variable["name"], grid_variable["name"], grid_variable["name"]
+ # if phasevol is <= 0 then we SKIP that whole loop. Its not working then.
+ if (
+ not grid_variable["dphasevol"] == -1
+ ): # A method to turn off this calculation and allow a phasevol = 1
+ code_string += (
+ indent * depth
+ + "phasevol_{} = sampled_values_{}[1]-sampled_values_{}[0]".format(
+ grid_variable["name"],
+ grid_variable["name"],
+ grid_variable["name"],
+ )
+ + "\n"
+ )
+ else:
+ code_string += indent * depth + "phasevol_{} = 1\n".format(
+ grid_variable["name"]
)
- + "\n"
- )
# # Some print statement
# code_string += (
@@ -1477,6 +1854,59 @@ class Population:
+ "\n"
)
+ #################################################################################
+ # Check condition and generate forloop
+
+ # If the grid variable has a condition, write the check and the action
+ if grid_variable["condition"]:
+ # Add comment
+ code_string += (
+ indent * (depth + 1)
+ + "# Condition for {}".format(grid_variable["parameter_name"])
+ + "\n"
+ )
+
+ # Add condition check
+ code_string += (
+ indent * (depth + 1)
+ + "if not {}:".format(grid_variable["condition"])
+ + "\n"
+ )
+
+ # Add condition failed action:
+ code_string += (
+ indent * (depth + 2)
+ + 'print("Grid generator: Condition for {} not met!")'.format(
+ grid_variable["parameter_name"]
+ )
+ + "\n"
+ )
+ code_string += indent * (depth + 2) + "continue" + "\n"
+
+ # Add some whiteline
+ code_string += indent * (depth + 1) + "\n"
+
+ ##############3
+ # Add phasevol check:
+ code_string += (
+ indent * (depth + 1)
+ + "if phasevol_{} <= 0:".format(grid_variable["name"])
+ + "\n"
+ )
+
+ # Add phasevol check action:
+ code_string += (
+ indent * (depth + 2)
+ + 'print("Grid generator: phasevol_{} <= 0!")'.format(
+ grid_variable["name"]
+ )
+ + "\n"
+ )
+ code_string += indent * (depth + 2) + "continue" + "\n"
+
+ # Add some whiteline
+ code_string += indent * (depth + 1) + "\n"
+
#########################
# Setting up pre-code and value in some cases
# Add pre-code
@@ -1503,7 +1933,7 @@ class Population:
code_string += indent * (depth + 1) + "# Setting probabilities\n"
code_string += (
indent * (depth + 1)
- + "d{} = phasevol_{} * {}".format(
+ + "d{} = phasevol_{} * ({})".format(
grid_variable["name"],
grid_variable["name"],
grid_variable["probdist"],
@@ -1570,96 +2000,33 @@ class Population:
# whether this is the last loop.
if loopnr == len(self.grid_options["_grid_variables"]) - 1:
- #################################################################################
- # Here are the calls to the queuing or other solution. this part is for every system
- # Add comment
- code_string += indent * (depth + 1) + "#" * 40 + "\n"
- code_string += (
- indent * (depth + 1)
- + "# Code below will get evaluated for every generated system\n"
+ code_string = self._write_gridcode_system_call(
+ code_string,
+ indent,
+ depth,
+ grid_variable,
+ dry_run,
+ grid_variable["branchpoint"],
)
- # Calculate value
- code_string += (
- indent * (depth + 1)
- + 'probability = self.grid_options["weight"] * probabilities_list[{}]'.format(
- grid_variable["grid_variable_number"]
- )
- + "\n"
- )
- # TODO: ask rob if just replacing this with probability is enough
- code_string += (
- indent * (depth + 1)
- # + 'repeat_probability = probability / self.grid_options["repeat"]'
- + 'probability = probability / self.grid_options["repeat"]'
- + "\n"
- )
-
- # For each repeat of the system this has to be done yes.
- code_string += (
- indent * (depth + 1)
- + 'for _ in range(self.grid_options["repeat"]):'
- + "\n"
- )
-
- code_string += indent * (depth + 2) + "_total_starcount += 1\n"
-
- # set probability and phasevol values
- code_string += (
- indent * (depth + 2)
- + 'parameter_dict["{}"] = {}'.format("probability", "probability")
- + "\n"
- )
- code_string += (
- indent * (depth + 2)
- + 'parameter_dict["{}"] = {}'.format("phasevol", "phasevol")
- + "\n"
- )
-
- # Some prints. will be removed
- # code_string += indent * (depth + 1) + "print(probabilities)\n"
- # code_string += (
- # indent * (depth + 1) + 'print("_total_starcount: ", _total_starcount)\n'
- # )
-
- # code_string += indent * (depth + 1) + "print(probability)\n"
-
- # Increment total probability
- code_string += (
- indent * (depth + 2) + "self._increment_probtot(probability)\n"
- )
-
- if not dry_run:
- # Handling of what is returned, or what is not.
- # TODO: think of whether this is a good method
- code_string += indent * (depth + 2) + "yield(parameter_dict)\n"
-
- # The below solution might be a good one to add things to specific queues
- # $self->queue_evolution_code_run($self->{_flexigrid}->{thread_q},
- # $system);
-
- # If its a dry run, dont do anything with it
- else:
- code_string += indent * (depth + 2) + "pass\n"
-
- code_string += indent * (depth + 1) + "#" * 40 + "\n"
-
# increment depth
depth += 1
depth -= 1
code_string += "\n"
+
# Write parts to write below the part that yield the results.
# 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,
- )
- ):
+ # Here we will have a
+ reverse_sorted_grid_variables = sorted(
+ self.grid_options["_grid_variables"].items(),
+ key=lambda x: x[1]["grid_variable_number"],
+ reverse=True,
+ )
+ for loopnr, grid_variable_el in enumerate(reverse_sorted_grid_variables):
grid_variable = grid_variable_el[1]
+
code_string += indent * (depth + 1) + "#" * 40 + "\n"
code_string += (
indent * (depth + 1)
@@ -1675,6 +2042,44 @@ class Population:
depth -= 1
+ # Check the branchpoint part here. The branchpoint makes sure that we can construct
+ # a grid with several multiplicities and still can make the system calls for each
+ # multiplicity without reconstructing the grid each time
+ if grid_variable["branchpoint"] > 0:
+
+ # Add comment
+ code_string += (
+ indent * (depth + 1)
+ + "# Condition for branchpoint at {}".format(
+ reverse_sorted_grid_variables[loopnr + 1][1]["parameter_name"]
+ )
+ + "\n"
+ )
+
+ # # Add condition check
+ # code_string += (
+ # indent * (depth + 1)
+ # + "if not {}:".format(grid_variable["condition"])
+ # + "\n"
+ # )
+
+ # Add branchpoint
+ code_string += (
+ indent * (depth + 1)
+ + "if multiplicity=={}:".format(grid_variable["branchpoint"])
+ + "\n"
+ )
+
+ code_string = self._write_gridcode_system_call(
+ code_string,
+ indent,
+ depth + 1,
+ reverse_sorted_grid_variables[loopnr + 1][1],
+ dry_run,
+ grid_variable["branchpoint"],
+ )
+ code_string += "\n"
+
################
# Finalising print statements
#
@@ -1722,6 +2127,91 @@ class Population:
with open(gridcode_filename, "w") as file:
file.write(code_string)
+ def _write_gridcode_system_call(
+ self, code_string, indent, depth, grid_variable, dry_run, branchpoint
+ ):
+ #################################################################################
+ # Here are the calls to the queuing or other solution. this part is for every system
+ # Add comment
+ code_string += indent * (depth + 1) + "#" * 40 + "\n"
+
+ if branchpoint:
+ code_string += (
+ indent * (depth + 1)
+ + "# Code below will get evaluated for every system at this level of multiplicity (last one of that being {})\n"
+ ).format(grid_variable["name"])
+ else:
+ code_string += (
+ indent * (depth + 1)
+ + "# Code below will get evaluated for every generated system\n"
+ )
+
+ # Calculate value
+ code_string += (
+ indent * (depth + 1)
+ + 'probability = self.grid_options["weight"] * probabilities_list[{}]'.format(
+ grid_variable["grid_variable_number"]
+ )
+ + "\n"
+ )
+ # TODO: ask rob if just replacing this with probability is enough
+ code_string += (
+ indent * (depth + 1)
+ # + 'repeat_probability = probability / self.grid_options["repeat"]'
+ + 'probability = probability / self.grid_options["repeat"]'
+ + "\n"
+ )
+
+ # For each repeat of the system this has to be done yes.
+ code_string += (
+ indent * (depth + 1) + 'for _ in range(self.grid_options["repeat"]):' + "\n"
+ )
+
+ code_string += indent * (depth + 2) + "_total_starcount += 1\n"
+
+ # set probability and phasevol values
+ code_string += (
+ indent * (depth + 2)
+ + 'parameter_dict["{}"] = {}'.format("probability", "probability")
+ + "\n"
+ )
+ code_string += (
+ indent * (depth + 2)
+ + 'parameter_dict["{}"] = {}'.format("phasevol", "phasevol")
+ + "\n"
+ )
+
+ # Some prints. will be removed
+ # code_string += indent * (depth + 1) + "print(probabilities)\n"
+ # code_string += (
+ # indent * (depth + 1) + 'print("_total_starcount: ", _total_starcount)\n'
+ # )
+
+ # code_string += indent * (depth + 1) + "print(probability)\n"
+
+ # Increment total probability
+ code_string += indent * (depth + 2) + "self._increment_probtot(probability)\n"
+
+ if not dry_run:
+ # Handling of what is returned, or what is not.
+ # TODO: think of whether this is a good method
+ # code_string += indent * (depth + 2) + "if (self.grid_options['multiplicity'] >= 2): print('phasevol_q: ',phasevol_q); print('phasevol_log10per: ',phasevol_log10per);\n"
+ # code_string += indent * (depth + 2) + "print('phasevol_lnm1: ',phasevol_lnm1); print('phasevol_multiplicity: ',phasevol_multiplicity);\n"
+ # code_string += indent * (depth + 2) + "print(probabilities_list)\n"
+ # code_string += indent * (depth + 2) + "print(parameter_dict)\n"
+ # code_string += indent * (depth + 2) + "print('YOO IK GA LEKKER NOG EEN RONDJE')\n"
+ code_string += indent * (depth + 2) + "yield(parameter_dict)\n"
+
+ # If its a dry run, dont do anything with it
+ else:
+ # code_string += indent * (depth + 2) + "if (self.grid_options['multiplicity'] >= 2): print(phasevol_q)\n"
+ # code_string += indent * (depth + 2) + "print(parameter_dict)\n"
+ code_string += indent * (depth + 2) + "pass\n"
+
+ code_string += indent * (depth + 1) + "#" * 40 + "\n"
+
+ return code_string
+
def _load_grid_function(self):
"""
Functon that loads the script containing the grid code.
@@ -2382,6 +2872,7 @@ class Population:
Tasks:
- TODO: test this function
+ - TODO: make sure the binary_c_python .. output file has a unique name
Args:
output_dir: (optional, default = None) directory where to write the file to. If custom_options['data_dir'] is present, then that one will be used first, and then the output_dir
@@ -2588,7 +3079,9 @@ class Population:
"""
if binary_c_output:
- if (binary_c_output.splitlines()[0].startswith("SYSTEM_ERROR")) or (binary_c_output.splitlines()[-1].startswith("SSYSTEM_ERROR")):
+ if (binary_c_output.splitlines()[0].startswith("SYSTEM_ERROR")) or (
+ binary_c_output.splitlines()[-1].startswith("SYSTEM_ERROR")
+ ):
verbose_print(
"FAILING SYSTEM FOUND",
self.grid_options["verbosity"],
@@ -2609,8 +3102,13 @@ class Population:
.strip()
)
- if not error_code in self.grid_options["_failed_systems_error_codes"]:
- self.grid_options["_failed_systems_error_codes"].append(error_code)
+ if (
+ not error_code
+ in self.grid_options["_failed_systems_error_codes"]
+ ):
+ self.grid_options["_failed_systems_error_codes"].append(
+ error_code
+ )
except ValueError:
verbose_print(
"Failed to extract the error-code",
@@ -2641,15 +3139,672 @@ class Population:
with open(
os.path.join(
self.grid_options["tmp_dir"],
- "failed_systems_{}_process_{}.txt".format(
- self.grid_options["_population_id"], self.process_ID
- ),
+ "failed_systems",
+ "process_{}.txt".format(self.process_ID),
),
"a+",
) as f:
f.write(argstring + "\n")
else:
verbose_print(
- "binary_c had 0 output. Which is strange", self.grid_options['verbosity'], 2)
+ "binary_c had 0 output. Which is strange",
+ self.grid_options["verbosity"],
+ 2,
+ )
+
+ ################################################################################################
+ def Moe_de_Stefano_2017(self, options=None):
+ """
+ Class method of the population class
+
+ Takes a dictionary as its only argument
+
+ The dictionary should contain a file to
+ """
+
+ # Require input options
+ if not options:
+ msg = "Please provide a options dictionary."
+ verbose_print(
+ "\tMoe_de_Stefano_2017: {}".format(msg),
+ self.grid_options["verbosity"],
+ 0,
+ )
+ raise ValueError(msg)
+
+ # Create the tmp dir
+ ms_tmp_dir = os.path.join(self.grid_options["tmp_dir"], "moe_distefano")
+ os.makedirs(ms_tmp_dir, exist_ok=True)
+
+ # TODO: put this in defaults
+ default_options = copy.deepcopy(moe_distefano_default_options)
+
+ # Take the option dictionary that was given and override.
+ options = update_dicts(default_options, options)
+
+ # Write options to a file
+ with open(os.path.join(ms_tmp_dir, "moeopts.dat"), "w") as f:
+ f.write(json.dumps(options, indent=4))
+
+ json_data = get_moe_distefano_dataset(options)
+
+ # entry of log10M1 is a list containing 1 dict. We can take the dict out of the list
+
+ json_data["log10M1"] = json_data["log10M1"][0]
+
+ # Get all the masses
+ logmasses = sorted(json_data["log10M1"].keys())
+ if not logmasses:
+ msg = "The table does not contain masses."
+ verbose_print(
+ "\tMoe_de_Stefano_2017: {}".format(msg),
+ self.grid_options["verbosity"],
+ 0,
+ )
+ raise ValueError(msg)
+
+ # Write to file
+ with open(os.path.join(ms_tmp_dir, "moe.log"), "w") as logfile:
+ logfile.write("logâ‚â‚€Masses(M☉) {}\n".format(logmasses))
+
+ # Get all the periods and see if they are all consistently present
+ logperiods = []
+ for logmass in logmasses:
+ if not logperiods:
+ logperiods = sorted(json_data["log10M1"][logmass]["logP"])
+ dlog10P = float(logperiods[1]) - float(logperiods[0])
+
+ current_logperiods = sorted(json_data["log10M1"][logmass]["logP"])
+
+ if not (logperiods == current_logperiods):
+ msg = "Period values are not consistent throughout the dataset"
+ verbose_print(
+ "\tMoe_de_Stefano_2017: {}".format(msg),
+ self.grid_options["verbosity"],
+ 0,
+ )
+ raise ValueError(msg)
+
+ ############################################################
+ # log10period binwidth : of course this assumes a fixed
+ # binwidth, so we check for this too.
+
+ for i in range(len(current_logperiods) - 1):
+ if not dlog10P == (
+ float(current_logperiods[i + 1]) - float(current_logperiods[i])
+ ):
+ msg = "Period spacing is not consistent throughout the dataset"
+ verbose_print(
+ "\tMoe_de_Stefano_2017: {}".format(msg),
+ self.grid_options["verbosity"],
+ 0,
+ )
+ raise ValueError(msg)
+
+ # Write to file
+ with open(
+ os.path.join(self.grid_options["tmp_dir"], "moe_distefano", "moe.log"), "a"
+ ) as logfile:
+ logfile.write("logâ‚â‚€Periods(days) {}\n".format(logperiods))
+
+ # Fill the global dict
+ for logmass in logmasses:
+ # Create the multiplicity table
+ if not Moecache.get("multiplicity_table", None):
+ Moecache["multiplicity_table"] = []
+
+ # multiplicity as a function of primary mass
+ Moecache["multiplicity_table"].append(
+ [
+ float(logmass),
+ json_data["log10M1"][logmass]["f_multi"],
+ json_data["log10M1"][logmass]["single star fraction"],
+ json_data["log10M1"][logmass]["binary star fraction"],
+ json_data["log10M1"][logmass]["triple/quad star fraction"],
+ ]
+ )
+
+ ############################################################
+ # a small log10period which we can shift just outside the
+ # table to force integration out there to zero
+ epslog10P = 1e-8 * dlog10P
+
+ ############################################################
+ # loop over either binary or triple-outer periods
+ first = 1
+
+ # Go over the periods
+ for logperiod in logperiods:
+ # print("logperiod: {}".format(logperiod))
+ ############################################################
+ # distributions of binary and triple star fractions
+ # as a function of mass, period.
+ #
+ # Note: these should be per unit log10P, hence we
+ # divide by $dlog10P
+
+ if first:
+ first = 0
+
+ # Create the multiplicity table
+ if not Moecache.get("period_distributions", None):
+ Moecache["period_distributions"] = []
+
+ ############################################################
+ # lower bound the period distributions to zero probability
+ Moecache["period_distributions"].append(
+ [
+ float(logmass),
+ float(logperiod) - 0.5 * dlog10P - epslog10P,
+ 0.0,
+ 0.0,
+ ]
+ )
+ Moecache["period_distributions"].append(
+ [
+ float(logmass),
+ float(logperiod) - 0.5 * dlog10P,
+ json_data["log10M1"][logmass]["logP"][logperiod][
+ "normed_bin_frac_p_dist"
+ ]
+ / dlog10P,
+ json_data["log10M1"][logmass]["logP"][logperiod][
+ "normed_tripquad_frac_p_dist"
+ ]
+ / dlog10P,
+ ]
+ )
+ Moecache["period_distributions"].append(
+ [
+ float(logmass),
+ float(logperiod),
+ json_data["log10M1"][logmass]["logP"][logperiod][
+ "normed_bin_frac_p_dist"
+ ]
+ / dlog10P,
+ json_data["log10M1"][logmass]["logP"][logperiod][
+ "normed_tripquad_frac_p_dist"
+ ]
+ / dlog10P,
+ ]
+ )
+
+ ############################################################
+ # distributions as a function of mass, period, q
+ #
+ # First, get a list of the qs given by Moe
+ #
+ qs = sorted(json_data["log10M1"][logmass]["logP"][logperiod]["q"])
+
+ # Fill the data and 'normalize'
+ qdata = fill_data(
+ qs, json_data["log10M1"][logmass]["logP"][logperiod]["q"]
+ )
+
+ # Create the multiplicity table
+ if not Moecache.get("q_distributions", None):
+ Moecache["q_distributions"] = []
+
+ for q in qs:
+ Moecache["q_distributions"].append(
+ [float(logmass), float(logperiod), float(q), qdata[q]]
+ )
+
+ ############################################################
+ # eccentricity distributions as a function of mass, period, ecc
+ eccs = sorted(json_data["log10M1"][logmass]["logP"][logperiod]["e"])
+
+ # Fill the data and 'normalize'
+ ecc_data = fill_data(
+ eccs, json_data["log10M1"][logmass]["logP"][logperiod]["e"]
+ )
+
+ # Create the multiplicity table
+ if not Moecache.get("ecc_distributions", None):
+ Moecache["ecc_distributions"] = []
+
+ for ecc in eccs:
+ Moecache["ecc_distributions"].append(
+ [float(logmass), float(logperiod), float(ecc), ecc_data[ecc]]
+ )
+
+ ############################################################
+ # upper bound the period distributions to zero probability
+ Moecache["period_distributions"].append(
+ [
+ float(logmass),
+ float(logperiods[-1]) + 0.5 * dlog10P, # TODO: why this shift?
+ json_data["log10M1"][logmass]["logP"][logperiods[-1]][
+ "normed_bin_frac_p_dist"
+ ]
+ / dlog10P,
+ json_data["log10M1"][logmass]["logP"][logperiods[-1]][
+ "normed_tripquad_frac_p_dist"
+ ]
+ / dlog10P,
+ ]
+ )
+ Moecache["period_distributions"].append(
+ [
+ float(logmass),
+ float(logperiods[-1]) + 0.5 * dlog10P + epslog10P,
+ 0.0,
+ 0.0,
+ ]
+ )
+
+ verbose_print(
+ "\tMoe_de_Stefano_2017: Length period_distributions table: {}".format(
+ len(Moecache["period_distributions"])
+ ),
+ self.grid_options["verbosity"],
+ _MS_VERBOSITY_LEVEL,
+ )
+ verbose_print(
+ "\tMoe_de_Stefano_2017: Length multiplicity table: {}".format(
+ len(Moecache["multiplicity_table"])
+ ),
+ self.grid_options["verbosity"],
+ _MS_VERBOSITY_LEVEL,
+ )
+ verbose_print(
+ "\tMoe_de_Stefano_2017: Length q table: {}".format(
+ len(Moecache["q_distributions"])
+ ),
+ self.grid_options["verbosity"],
+ _MS_VERBOSITY_LEVEL,
+ )
+ verbose_print(
+ "\tMoe_de_Stefano_2017: Length ecc table: {}".format(
+ len(Moecache["ecc_distributions"])
+ ),
+ self.grid_options["verbosity"],
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Write to logfile
+ with open(os.path.join(ms_tmp_dir, "moecache.json"), "w") as cache_filehandle:
+ cache_filehandle.write(json.dumps(Moecache, indent=4))
+
+
+ ############################################################
+ # construct the grid here
+ #
+
+ ############################################################
+ # make a version of the options which can be passed to
+ # the probability density function in the gridcode
+
+ ############################################################
+ # first, the multiplicity, this is 1,2,3,4, ...
+ # for singles, binaries, triples, quadruples, ...
+
+ max_multiplicity = get_max_multiplicity(options["multiplicity_modulator"])
+ verbose_print(
+ "\tMoe_de_Stefano_2017: Max multiplicity = {}".format(max_multiplicity),
+ self.grid_options["verbosity"],
+ _MS_VERBOSITY_LEVEL,
+ )
+ ######
+ # Setting up the grid variables
+
+ # Multiplicity
+ self.add_grid_variable(
+ name="multiplicity",
+ parameter_name="multiplicity",
+ longname="multiplicity",
+ valuerange=[1, max_multiplicity],
+ resolution=1,
+ spacingfunc="range(1, 5)",
+ precode='self.grid_options["multiplicity"] = multiplicity; options={}'.format(
+ options
+ ),
+ condition="({}[multiplicity-1] > 0)".format(
+ str(options["multiplicity_modulator"])
+ ),
+ gridtype="edge",
+ dphasevol=-1,
+ probdist=1,
+ )
+
+ ############################################################
+ # always require M1, for all systems
+ #
+
+ # TODO: put in option for the time-adaptive grid.
+
+ # log-spaced m1 with given resolution
+ self.add_grid_variable(
+ name="lnm1",
+ parameter_name="M_1",
+ longname="Primary mass",
+ resolution="options['resolutions']['M'][0]",
+ spacingfunc="const(np.log({}), np.log({}), {})".format(
+ options["ranges"]["M"][0],
+ options["ranges"]["M"][1],
+ options["resolutions"]["M"][0],
+ ),
+ valuerange=[
+ "np.log({})".format(options["ranges"]["M"][0]),
+ "np.log({})".format(options["ranges"]["M"][1]),
+ ],
+ gridtype="centred",
+ dphasevol="dlnm1",
+ precode='M_1 = np.exp(lnm1); options["M1"]=M_1',
+ probdist="Moe_de_Stefano_2017_pdf({{{}, {}, {}}}, verbosity=self.grid_options['verbosity'])['total_probdens'] if multiplicity == 1 else 1".format(
+ str(options)[1:-1], "'multiplicity': multiplicity", "'M1': M_1"
+ ),
+ )
+
+ # Go to higher multiplicities
+ if max_multiplicity >= 2:
+ # binaries: period
+ self.add_grid_variable(
+ name="log10per",
+ parameter_name="orbital_period",
+ longname="log10(Orbital_Period)",
+ resolution=options["resolutions"]["logP"][0],
+ probdist=1.0,
+ condition='(self.grid_options["multiplicity"] >= 2)',
+ branchpoint=1 if max_multiplicity > 1 else 0, # Signal here to put a branchpoint if we have a max multiplicity higher than 1.
+ gridtype="centred",
+ dphasevol="({} * dlog10per)".format(LOG_LN_CONVERTER),
+ valuerange=[options["ranges"]["logP"][0], options["ranges"]["logP"][1]],
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["logP"][0],
+ options["ranges"]["logP"][1],
+ options["resolutions"]["logP"][0],
+ ),
+ precode="""orbital_period = 10.0**log10per
+qmin={}/M_1
+qmax=maximum_mass_ratio_for_RLOF(M_1, orbital_period)
+""".format(
+ options.get("Mmin", 0.07)
+ ),
+ ) # TODO: change the maximum_mass_ratio_for_RLOF
+
+ # binaries: mass ratio
+ self.add_grid_variable(
+ name="q",
+ parameter_name="M_2",
+ longname="Mass ratio",
+ valuerange=[
+ options["ranges"]["q"][0]
+ if options.get("ranges", {}).get("q", None)
+ else "options.get('Mmin', 0.07)/M_1",
+ options["ranges"]["q"][1]
+ if options.get("ranges", {}).get("q", None)
+ else "qmax",
+ ],
+ resolution=options["resolutions"]["M"][1],
+ probdist=1,
+ gridtype="centred",
+ dphasevol="dq",
+ precode="""
+M_2 = q * M_1
+sep = calc_sep_from_period(M_1, M_2, orbital_period)
+ """,
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["q"][0]
+ if options.get("ranges", {}).get("q", [None, None])[0]
+ else "{}/M_1".format(options.get("Mmin", 0.07)),
+ options["ranges"]["q"][1]
+ if options.get("ranges", {}).get("q", [None, None])[1]
+ else "qmax",
+ options["resolutions"]["M"][1],
+ ),
+ )
+
+ # (optional) binaries: eccentricity
+ if options["resolutions"]["ecc"][0] > 0:
+ self.add_grid_variable(
+ name="ecc",
+ parameter_name="eccentricity",
+ longname="Eccentricity",
+ resolution=options["resolutions"]["ecc"][0],
+ probdist=1,
+ gridtype="centred",
+ dphasevol="decc",
+ precode="eccentricity=ecc",
+ valuerange=[
+ options["ranges"]["ecc"][0], # Just fail if not defined.
+ options["ranges"]["ecc"][1],
+ ],
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["ecc"][0], # Just fail if not defined.
+ options["ranges"]["ecc"][1],
+ options["resolutions"]["ecc"][0],
+ ),
+ )
+
+ # Now for triples and quadruples
+ if max_multiplicity >= 3:
+ # Triple: period
+ self.add_grid_variable(
+ name="log10per2",
+ parameter_name="orbital_period_triple",
+ longname="log10(Orbital_Period2)",
+ resolution=options["resolutions"]["logP"][1],
+ probdist=1.0,
+ condition='(self.grid_options["multiplicity"] >= 3)',
+ branchpoint=2 if max_multiplicity > 2 else 0, # Signal here to put a branchpoint if we have a max multiplicity higher than 1.
+ gridtype="centred",
+ dphasevol="({} * dlog10per2)".format(LOG_LN_CONVERTER),
+ valuerange=[
+ options["ranges"]["logP"][0],
+ options["ranges"]["logP"][1],
+ ],
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["logP"][0],
+ options["ranges"]["logP"][1],
+ options["resolutions"]["logP"][1],
+ ),
+ precode="""orbital_period_triple = 10.0**log10per2
+q2min={}/(M_1+M_2)
+q2max=maximum_mass_ratio_for_RLOF(M_1+M_2, orbital_period_triple)
+ """.format(
+ options.get("Mmin", 0.07)
+ ),
+ )
+
+ # Triples: mass ratio
+ # Note, the mass ratio is M_outer/M_inner
+ print("M2 resolution {}".format(options["resolutions"]["M"][2]))
+ self.add_grid_variable(
+ name="q2",
+ parameter_name="M_3",
+ longname="Mass ratio outer/inner",
+ valuerange=[
+ options["ranges"]["q"][0]
+ if options.get("ranges", {}).get("q", None)
+ else "options.get('Mmin', 0.07)/(M_1+M_2)",
+ options["ranges"]["q"][1]
+ if options.get("ranges", {}).get("q", None)
+ else "q2max",
+ ],
+ resolution=options["resolutions"]["M"][2],
+ probdist=1,
+ gridtype="centred",
+ dphasevol="dq2",
+ precode="""
+M_3 = q2 * (M_1 + M_2)
+sep2 = calc_sep_from_period((M_1+M_2), M_3, orbital_period_triple)
+eccentricity2=0
+""",
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["q"][0]
+ if options.get("ranges", {}).get("q", None)
+ else "options.get('Mmin', 0.07)/(M_1+M_2)",
+ options["ranges"]["q"][1]
+ if options.get("ranges", {}).get("q", None)
+ else "q2max",
+ options["resolutions"]["M"][2],
+ ),
+ )
+
+ # (optional) triples: eccentricity
+ if options["resolutions"]["ecc"][1] > 0:
+ self.add_grid_variable(
+ name="ecc2",
+ parameter_name="eccentricity2",
+ longname="Eccentricity of the triple",
+ resolution=options["resolutions"]["ecc"][1],
+ probdist=1,
+ gridtype="centred",
+ dphasevol="decc2",
+ precode="eccentricity2=ecc2",
+ valuerange=[
+ options["ranges"]["ecc"][0], # Just fail if not defined.
+ options["ranges"]["ecc"][1],
+ ],
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["ecc"][0], # Just fail if not defined.
+ options["ranges"]["ecc"][1],
+ options["resolutions"]["ecc"][1],
+ ),
+ )
+
+ if max_multiplicity == 4:
+ # Quadruple: period
+ self.add_grid_variable(
+ name="log10per3",
+ parameter_name="orbital_period_quadruple",
+ longname="log10(Orbital_Period3)",
+ resolution=options["resolutions"]["logP"][2],
+ probdist=1.0,
+ condition='(self.grid_options["multiplicity"] >= 4)',
+ branchpoint=3 if max_multiplicity > 3 else 0, # Signal here to put a branchpoint if we have a max multiplicity higher than 1.
+ gridtype="centred",
+ dphasevol="({} * dlog10per3)".format(LOG_LN_CONVERTER),
+ valuerange=[
+ options["ranges"]["logP"][0],
+ options["ranges"]["logP"][1],
+ ],
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["logP"][0],
+ options["ranges"]["logP"][1],
+ options["resolutions"]["logP"][2],
+ ),
+ precode="""orbital_period_quadruple = 10.0**log10per3
+q3min={}/(M_3)
+q3max=maximum_mass_ratio_for_RLOF(M_3, orbital_period_quadruple)
+ """.format(
+ options.get("Mmin", 0.07)
+ ),
+ )
+
+ # Quadruple: mass ratio : M_outer / M_inner
+ print("M3 resolution {}".format(options["resolutions"]["M"][3]))
+ self.add_grid_variable(
+ name="q3",
+ parameter_name="M_4",
+ longname="Mass ratio outer low/outer high",
+ valuerange=[
+ options["ranges"]["q"][0]
+ if options.get("ranges", {}).get("q", None)
+ else "options.get('Mmin', 0.07)/(m3)",
+ options["ranges"]["q"][1]
+ if options.get("ranges", {}).get("q", None)
+ else "q3max",
+ ],
+ resolution=options["resolutions"]["M"][3],
+ probdist=1,
+ gridtype="centred",
+ dphasevol="dq3",
+ precode="""
+M_4 = q3 * M_3
+sep3 = calc_sep_from_period((M_3), M_4, orbital_period_quadruple)
+eccentricity3=0
+ """,
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["q"][0]
+ if options.get("ranges", {}).get("q", None)
+ else "options.get('Mmin', 0.07)/(m3)",
+ options["ranges"]["q"][1]
+ if options.get("ranges", {}).get("q", None)
+ else "q3max",
+ options["resolutions"]["M"][2],
+ ),
+ )
+ # TODO: Ask about which periods should be used for the calc_sep_from_period
+
+ # (optional) triples: eccentricity
+ if options["resolutions"]["ecc"][2] > 0:
+ self.add_grid_variable(
+ name="ecc3",
+ parameter_name="eccentricity3",
+ longname="Eccentricity of the triple+quadruple/outer binary",
+ resolution=options["resolutions"]["ecc"][2],
+ probdist=1,
+ gridtype="centred",
+ dphasevol="decc3",
+ precode="eccentricity3=ecc3",
+ valuerange=[
+ options["ranges"]["ecc"][
+ 0
+ ], # Just fail if not defined.
+ options["ranges"]["ecc"][1],
+ ],
+ spacingfunc="const({}, {}, {})".format(
+ options["ranges"]["ecc"][
+ 0
+ ], # Just fail if not defined.
+ options["ranges"]["ecc"][1],
+ options["resolutions"]["ecc"][2],
+ ),
+ )
+
+ # Now we are at the last part.
+ # Here we should combine all the information that we calculate and update the options
+ # dictionary. This will then be passed to the Moe_de_Stefano_2017_pdf to calculate
+ # the real probability. The trick we use is to strip the options_dict as a string
+ # and add some keys to it:
+
+ probdist_addition = "Moe_de_Stefano_2017_pdf({{{}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}}}, verbosity=self.grid_options['verbosity'])['total_probdens']".format(
+ str(options)[1:-1],
+ '"multiplicity": multiplicity',
+ '"M1": M_1',
+ '"M2": M_2',
+ '"M3": M_3',
+ '"M4": M_4',
+ '"P": orbital_period',
+ '"P2": orbital_period_triple',
+ '"P3": orbital_period_quadruple',
+ '"ecc": eccentricity',
+ '"ecc2": eccentricity2',
+ '"ecc3": eccentricity3',
+ )
+
+ # and finally the probability calculator
+ self.grid_options["_grid_variables"][self.last_grid_variable()][
+ "probdist"
+ ] = probdist_addition
+
+ verbose_print(
+ "\tMoe_de_Stefano_2017: Added final call to the pdf function".format(
+ max_multiplicity
+ ),
+ self.grid_options["verbosity"],
+ _MS_VERBOSITY_LEVEL,
+ )
+
+ # Signal that the M&S grid has been set
+ self.grid_options['_set_ms_grid'] = True
+
+ def _clean_interpolators(self):
+ """
+ Function to clean up the interpolators after a run
+
+ We look in the MoeCache global variable for items that are interpolators.
+ Should be called by the general cleanup function AND the thread cleanup function
+ """
+
+ interpolator_keys = []
+ for key in Moecache.keys():
+ if isinstance(Moecache[key], py_rinterpolate.Rinterpolate):
+ interpolator_keys.append(key)
-################################################################################################
+ for key in interpolator_keys:
+ Moecache[key].destroy()
+ del Moecache[key]
+ gc.collect()
diff --git a/binarycpython/utils/grid_options_defaults.py b/binarycpython/utils/grid_options_defaults.py
index ac9d12b00689673ad7f579946cd7560ce7f23ed9..fee8212e03fdb2feb8d25173dce277a36aa848e8 100644
--- a/binarycpython/utils/grid_options_defaults.py
+++ b/binarycpython/utils/grid_options_defaults.py
@@ -11,11 +11,19 @@ There are several other functions in this module, mostly to generate help texts
With this its also possible to automatically generate a document containing all the setting names + descriptions.
All the options starting with _ should not be changed by the user except when you really know what you're doing (which is probably hacking the code :P)
+
+# TODO: add moe distefano
"""
import os
from binarycpython.utils.custom_logging_functions import temp_dir
+from binarycpython.utils.functions import return_binary_c_version_info
+
+_MS_VERBOSITY_LEVEL = 5
+_MS_VERBOSITY_INTERPOLATOR_LEVEL = 6
+_MS_VERBOSITY_INTERPOLATOR_EXTRA_LEVEL = 7
+
# Options dict
grid_options_defaults_dict = {
@@ -30,11 +38,16 @@ grid_options_defaults_dict = {
"combine_ensemble_with_thread_joining": True, # Flag on whether to combine everything and return it to the user or if false: write it to data_dir/ensemble_output_{popuation_id}_{thread_id}.json
# "output_dir":
"_commandline_input": "",
- "log_runtime_systems": 0, # whether to log the runtime of the systems (1 file per thread. stored in the tmp_dir)
+ "log_runtime_systems": 0, # whether to log the runtime of the systems (1 file per thread. stored in the tmp_dir)
+ "_actually_evolve_system": True, # Whether to actually evolve the systems of just act as if. for testing. used in _process_run_population_grid
+ "max_queue_size": 1000, # Maximum size of the system call queue. Can't be too big!
+ "_set_ms_grid": False, # Whether the M&S grid has been loaded
+ "run_zero_probability_system": True, # Whether to run the zero probability systems
+ "_zero_prob_stars_skipped": 0,
##########################
# Execution log:
##########################
- "verbosity": 0, # Level of verbosity of the simulation. 0=INFO,
+ "verbosity": 0, # Level of verbosity of the simulation
"log_file": os.path.join(
temp_dir(), "binary_c_python.log"
), # Set to None to not log to file. The directory will be created
@@ -95,6 +108,8 @@ grid_options_defaults_dict = {
"failed_systems_threshold": 20, # Maximum failed systems per process allowed to fail before the process stops logging the failing systems.
"_failed_systems_error_codes": [], # List to store the unique error codes
"_population_id": 0, # Random id of this grid/population run, Unique code for the population. Should be set only once by the controller process.
+ "_total_mass_run": 0, # To count the total mass that thread/process has ran
+ "_total_probability_weighted_mass_run": 0, # To count the total mass * probability for each system that thread/process has ran
"modulo": 1, # run modulo n of the grid. #TODO: fix this
## Grid type evolution
"_grid_variables": {}, # grid variables
@@ -463,8 +478,183 @@ grid_options_descriptions = {
"evolution_type": "Variable containing the type of evolution used of the grid. Multiprocessing or linear processing",
"combine_ensemble_with_thread_joining": "BOolean flag on whether to combine everything and return it to the user or if false: write it to data_dir/ensemble_output_{popuation_id}_{thread_id}.json",
"log_runtime_systems": "Whether to log the runtime of the systems . Each systems run by the thread is logged to a file and is stored in the tmp_dir. (1 file per thread). Don't use this if you are planning to run alot of systems. This is mostly for debugging and finding systems that take long to run. Integer, default = 0. if value is 1 then the systems are logged",
+ "_total_mass_run": "To count the total mass that thread/process has ran",
+ "_total_probability_weighted_mass_run": "To count the total mass * probability for each system that thread/process has ran",
+ "_actually_evolve_system": "Whether to actually evolve the systems of just act as if. for testing. used in _process_run_population_grid",
+ "max_queue_size": "Maximum size of the queue that is used to feed the processes. Don't make this too big! Default: 1000. Input: int",
+ "_set_ms_grid": "Internal flag whether the M&S grid has been loaded",
+ "run_zero_probability_system": "Whether to run the zero probability systems. Default: True. Input: boolean",
+ "_zero_prob_stars_skipped": "Internal counter to track how many systems are skipped because they have 0 probability"
+}
+
+###
+#
+
+# Default options for the Moe & Distefano grid
+moe_distefano_default_options = {
+ "resolutions": {
+ "M": [
+ 20, # M1
+ 20, # M2 (i.e. q)
+ 0, # M3 currently unused
+ 0, # M4 currently unused
+ ],
+ "logP": [
+ 20, # P2 (binary period)
+ 0, # P3 (triple period) currently unused
+ 0, # P4 (quadruple period) currently unused
+ ],
+ "ecc": [
+ 10, # e (binary eccentricity)
+ 0, # e2 (triple eccentricity) currently unused
+ 0, # e3 (quadruple eccentricity) currently unused
+ ],
+ },
+ "ranges": {
+ # stellar masses (Msun)
+ "M": [
+ 0.08, # 0.08 is a tad bit above the minimum mass. Don't sample at 0.07, otherwise the first row of q values will have a phasevol of 0. Anything higher is fine.
+ 80.0, # (rather arbitrary) upper mass cutoff
+ ],
+ "q": [
+ None, # artificial qmin : set to None to use default
+ None, # artificial qmax : set to None to use default
+ ],
+ "logP": [0.0, 8.0], # 0 = log10(1 day) # 8 = log10(10^8 days)
+ "ecc": [0.0, 0.99],
+ },
+ # minimum stellar mass
+ "Mmin": float(return_binary_c_version_info(parsed=True)['macros']['BINARY_C_MINIMUM_STELLAR_MASS']), # We take the value that binary_c has set as the default
+
+ # multiplicity model (as a function of log10M1)
+ #
+ # You can use 'Poisson' which uses the system multiplicty
+ # given by Moe and maps this to single/binary/triple/quad
+ # fractions.
+ #
+ # Alternatively, 'data' takes the fractions directly
+ # from the data, but then triples and quadruples are
+ # combined (and there are NO quadruples).
+ "multiplicity_model": "Poisson",
+
+ # multiplicity modulator:
+ # [single, binary, triple, quadruple]
+ #
+ # e.g. [1,0,0,0] for single stars only
+ # [0,1,0,0] for binary stars only
+ #
+ # defaults to [1,1,0,0] i.e. all types
+ #
+ "multiplicity_modulator": [
+ 1, # single
+ 1, # binary
+ 0, # triple
+ 0, # quadruple
+ ],
+ # given a mix of multiplities, you can either (noting that
+ # here (S,B,T,Q) = appropriate modulator * model(S,B,T,Q) )
+ #
+ # 'norm' : normalize so the whole population is 1.0
+ # after implementing the appropriate fractions
+ # S/(S+B+T+Q), B/(S+B+T+Q), T/(S+B+T+Q), Q/(S+B+T+Q)
+ #
+ # 'raw' : stick to what is predicted, i.e.
+ # S/(S+B+T+Q), B/(S+B+T+Q), T/(S+B+T+Q), Q/(S+B+T+Q)
+ # without normalization
+ # (in which case the total probability < 1.0 unless
+ # all you use single, binary, triple and quadruple)
+ #
+ # 'merge' : e.g. if you only have single and binary,
+ # add the triples and quadruples to the binaries, so
+ # binaries represent all multiple systems
+ # ...
+ # *** this is canonical binary population synthesis ***
+ #
+ # Note: if multiplicity_modulator == [1,1,1,1] this
+ # option does nothing (equivalent to 'raw').
+ #
+ #
+ # note: if you only set one multiplicity_modulator
+ # to 1, and all the others to 0, then normalizing
+ # will mean that you effectively have the same number
+ # of stars as single, binary, triple or quad (whichever
+ # is non-zero) i.e. the multiplicity fraction is ignored.
+ # This is probably not useful except for
+ # testing purposes or comparing to old grids.
+ "normalize_multiplicities": "merge",
+ # q extrapolation (below 0.15 and above 0.9) method. We can choose from ['flat', 'linear', 'plaw2', 'nolowq']
+ "q_low_extrapolation_method": "linear",
+ "q_high_extrapolation_method": "linear",
+}
+
+moe_distefano_default_options_description = {
+ "resolutions": "",
+ "ranges": "",
+ "Mmin": "Minimum stellar mass",
+ "multiplicity_model": """
+multiplicity model (as a function of log10M1)
+
+You can use 'Poisson' which uses the system multiplicty
+given by Moe and maps this to single/binary/triple/quad
+fractions.
+
+Alternatively, 'data' takes the fractions directly
+from the data, but then triples and quadruples are
+combined (and there are NO quadruples).
+""",
+ "multiplicity_modulator": """
+[single, binary, triple, quadruple]
+
+e.g. [1,0,0,0] for single stars only
+ [0,1,0,0] for binary stars only
+
+defaults to [1,1,0,0] i.e. singles and binaries
+""",
+ "normalize_multiplicities": """
+'norm': normalize so the whole population is 1.0
+ after implementing the appropriate fractions
+ S/(S+B+T+Q), B/(S+B+T+Q), T/(S+B+T+Q), Q/(S+B+T+Q)
+ given a mix of multiplities, you can either (noting that
+ here (S,B,T,Q) = appropriate modulator * model(S,B,T,Q) )
+ note: if you only set one multiplicity_modulator
+ to 1, and all the others to 0, then normalizing
+ will mean that you effectively have the same number
+ of stars as single, binary, triple or quad (whichever
+ is non-zero) i.e. the multiplicity fraction is ignored.
+ This is probably not useful except for
+ testing purposes or comparing to old grids.
+
+'raw' : stick to what is predicted, i.e.
+ S/(S+B+T+Q), B/(S+B+T+Q), T/(S+B+T+Q), Q/(S+B+T+Q)
+ without normalization
+ (in which case the total probability < 1.0 unless
+ all you use single, binary, triple and quadruple)
+
+'merge' : e.g. if you only have single and binary,
+ add the triples and quadruples to the binaries, so
+ binaries represent all multiple systems
+ ...
+ *** this is canonical binary population synthesis ***
+
+ It only takes the maximum multiplicity into account,
+ i.e. it doesn't multiply the resulting array by the multiplicity modulator again.
+ This prevents the resulting array to always be 1 if only 1 multiplicity modulator element is nonzero
+
+ Note: if multiplicity_modulator == [1,1,1,1] this
+ option does nothing (equivalent to 'raw').
+""",
+ "q_low_extrapolation_method": """
+q extrapolation (below 0.15) method
+ none
+ flat
+ linear2
+ plaw2
+ nolowq
+""",
+ "q_high_extrapolation_method": "Same as q_low_extrapolation_method",
}
+
#################################
# Grid options functions
@@ -578,41 +768,49 @@ def write_grid_options_to_rst_file(output_file: str) -> None:
print("\n", file=f)
# Start public options part
- print("Public options", file=f)
- print("{}".format("-" * len("Public options")), file=f)
- print("The following options are meant to be changed by the user.", file=f)
- print("\n", file=f)
+ print_option_descriptions(
+ f,
+ public_options,
+ descriptions,
+ "Public options",
+ "The following options are meant to be changed by the user.",
+ )
- for public_option in sorted(public_options):
- if public_option in descriptions:
- print(
- "| **{}**: {}".format(public_option, descriptions[public_option]),
- file=f,
- )
- else:
- print(
- "| **{}**: No description available yet".format(public_option),
- file=f,
- )
- print("", file=f)
+ # Moe & Distefano options:
+ print_option_descriptions(
+ f,
+ moe_distefano_default_options,
+ moe_distefano_default_options_description,
+ "Moe & Distefano sampler options",
+ "The following options are meant to be changed by the user.",
+ )
# Start private options part
- print("Private options", file=f)
- print("{}".format("-" * len("Private options")), file=f)
- print(
+ print_option_descriptions(
+ f,
+ private_options,
+ descriptions,
+ "Private options",
"The following options are not meant to be changed by the user, as these options are used and set internally by the object itself. The description still is provided, but just for documentation purposes.",
- file=f,
)
- for private_option in sorted(private_options):
- if private_option in descriptions:
- print(
- "| **{}**: {}".format(private_option, descriptions[private_option]),
- file=f,
- )
- else:
- print(
- "| **{}**: No description available yet".format(private_option),
- file=f,
- )
- print("", file=f)
+
+def print_option_descriptions(filehandle, options, descriptions, title, extra_text):
+ # Start public options part
+ print("{}".format(title), file=filehandle)
+ print("{}".format("-" * len("{}".format(title))), file=filehandle)
+ print("{}".format(extra_text), file=filehandle)
+ print("\n", file=filehandle)
+
+ for option in sorted(options):
+ if option in descriptions:
+ print(
+ "| **{}**: {}".format(option, descriptions[option]),
+ file=filehandle,
+ )
+ else:
+ print(
+ "| **{}**: No description available yet".format(option),
+ file=filehandle,
+ )
+ print("", file=filehandle)
diff --git a/binarycpython/utils/moe_distefano_data.py b/binarycpython/utils/moe_distefano_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d8b8a65a1033aa52d66a94d5c0124980e2d394d
--- /dev/null
+++ b/binarycpython/utils/moe_distefano_data.py
@@ -0,0 +1,12944 @@
+"""
+File containing dictionary with the Moe & diStefano 2017 dataset. Prepared by Sophie Dykes
+"""
+
+moe_distefano_2017_data = {
+ "Comments": "File contains data concerning properties for different log10(primary mass) for multiple star systems. Data from Moe and Di Stefano (2017): https://ui.adsabs.harvard.edu/abs/2017ApJS..230...15M/abstract . Github: https://gitlab.eps.surrey.ac.uk/sd00599/fyp",
+ "log10M1": [
+ {
+ "-1.0": {
+ "f_multi": 0.1126,
+ "multi system fraction": 0.0995,
+ "binary star fraction": 0.0929,
+ "triple/quad star fraction": 0.0066,
+ "single star fraction": 0.9005,
+ "poisson_model": {
+ "single_fraction": 0.894,
+ "binary_fraction": 0.101,
+ "triple_fraction": 0.00566,
+ "quadruple_fraction": 2.13e-05,
+ },
+ "logP": {
+ "0.25": {
+ "periodfrac": 0.0026031,
+ "P_bin": 1.0,
+ "normed_bin_frac_p_dist": 0.02617,
+ "normed_tripquad_frac_p_dist": 0.0,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.423083,
+ "0.95": 0.576917,
+ },
+ "e": {
+ "0.05": 1.0,
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.0,
+ "0.95": 0.0,
+ },
+ },
+ "0.75": {
+ "periodfrac": 0.00328462,
+ "P_bin": 1.0,
+ "normed_bin_frac_p_dist": 0.03302,
+ "normed_tripquad_frac_p_dist": 0.0,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.423083,
+ "0.95": 0.576917,
+ },
+ "e": {
+ "0.05": 1.0,
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.0,
+ "0.95": 0.0,
+ },
+ },
+ "1.25": {
+ "periodfrac": 0.00426667,
+ "P_bin": 1.0,
+ "normed_bin_frac_p_dist": 0.04289,
+ "normed_tripquad_frac_p_dist": 0.0,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.423083,
+ "0.95": 0.576917,
+ },
+ "e": {
+ "0.05": 0.330824,
+ "0.15": 0.165642,
+ "0.25": 0.129153,
+ "0.35": 0.110009,
+ "0.45": 0.100964,
+ "0.55": 0.0990032,
+ "0.65": 0.057533,
+ "0.75": 0.00687094,
+ "0.85": 0.0,
+ "0.95": 0.0,
+ },
+ },
+ "1.75": {
+ "periodfrac": 0.0055291,
+ "P_bin": 0.995371,
+ "normed_bin_frac_p_dist": 0.05532,
+ "normed_tripquad_frac_p_dist": 0.001953,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.423083,
+ "0.95": 0.576917,
+ },
+ "e": {
+ "0.05": 0.14167,
+ "0.15": 0.131895,
+ "0.25": 0.12826,
+ "0.35": 0.125956,
+ "0.45": 0.12427,
+ "0.55": 0.123559,
+ "0.65": 0.12234,
+ "0.75": 0.0809714,
+ "0.85": 0.0210788,
+ "0.95": 0.0,
+ },
+ },
+ "2.25": {
+ "periodfrac": 0.00704218,
+ "P_bin": 0.981652,
+ "normed_bin_frac_p_dist": 0.06949,
+ "normed_tripquad_frac_p_dist": 0.009859,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.424963,
+ "0.95": 0.575037,
+ },
+ "e": {
+ "0.05": 0.0944499,
+ "0.15": 0.110031,
+ "0.25": 0.117032,
+ "0.35": 0.121823,
+ "0.45": 0.125515,
+ "0.55": 0.127863,
+ "0.65": 0.127994,
+ "0.75": 0.112768,
+ "0.85": 0.0563073,
+ "0.95": 0.00621564,
+ },
+ },
+ "2.75": {
+ "periodfrac": 0.0086958,
+ "P_bin": 0.963641,
+ "normed_bin_frac_p_dist": 0.08423,
+ "normed_tripquad_frac_p_dist": 0.02412,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.426869,
+ "0.95": 0.573131,
+ },
+ "e": {
+ "0.05": 0.0754871,
+ "0.15": 0.0987081,
+ "0.25": 0.110213,
+ "0.35": 0.118419,
+ "0.45": 0.12492,
+ "0.55": 0.129746,
+ "0.65": 0.13039,
+ "0.75": 0.123258,
+ "0.85": 0.0725423,
+ "0.95": 0.0163158,
+ },
+ },
+ "3.25": {
+ "periodfrac": 0.0101032,
+ "P_bin": 0.942517,
+ "normed_bin_frac_p_dist": 0.09572,
+ "normed_tripquad_frac_p_dist": 0.04431,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.429069,
+ "0.95": 0.570931,
+ },
+ "e": {
+ "0.05": 0.0657833,
+ "0.15": 0.0922986,
+ "0.25": 0.106244,
+ "0.35": 0.116451,
+ "0.45": 0.124683,
+ "0.55": 0.131158,
+ "0.65": 0.132341,
+ "0.75": 0.127869,
+ "0.85": 0.0804725,
+ "0.95": 0.0226993,
+ },
+ },
+ "3.75": {
+ "periodfrac": 0.0110051,
+ "P_bin": 0.91881,
+ "normed_bin_frac_p_dist": 0.1016,
+ "normed_tripquad_frac_p_dist": 0.06818,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.431586,
+ "0.95": 0.568414,
+ },
+ "e": {
+ "0.05": 0.0601091,
+ "0.15": 0.0884375,
+ "0.25": 0.103949,
+ "0.35": 0.115505,
+ "0.45": 0.124938,
+ "0.55": 0.132423,
+ "0.65": 0.133797,
+ "0.75": 0.13029,
+ "0.85": 0.0845054,
+ "0.95": 0.0260463,
+ },
+ },
+ "4.25": {
+ "periodfrac": 0.0113538,
+ "P_bin": 0.89282,
+ "normed_bin_frac_p_dist": 0.1019,
+ "normed_tripquad_frac_p_dist": 0.09285,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.434447,
+ "0.95": 0.565553,
+ },
+ "e": {
+ "0.05": 0.0563761,
+ "0.15": 0.085828,
+ "0.25": 0.102429,
+ "0.35": 0.114957,
+ "0.45": 0.125274,
+ "0.55": 0.133513,
+ "0.65": 0.135029,
+ "0.75": 0.131961,
+ "0.85": 0.086745,
+ "0.95": 0.0278885,
+ },
+ },
+ "4.75": {
+ "periodfrac": 0.0111577,
+ "P_bin": 0.864737,
+ "normed_bin_frac_p_dist": 0.09698,
+ "normed_tripquad_frac_p_dist": 0.1152,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.437684,
+ "0.95": 0.562316,
+ },
+ "e": {
+ "0.05": 0.0537433,
+ "0.15": 0.0839551,
+ "0.25": 0.101361,
+ "0.35": 0.114626,
+ "0.45": 0.125623,
+ "0.55": 0.134449,
+ "0.65": 0.136076,
+ "0.75": 0.133203,
+ "0.85": 0.088098,
+ "0.95": 0.0288651,
+ },
+ },
+ "5.25": {
+ "periodfrac": 0.0104133,
+ "P_bin": 0.834686,
+ "normed_bin_frac_p_dist": 0.08737,
+ "normed_tripquad_frac_p_dist": 0.1314,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.441333,
+ "0.95": 0.558667,
+ },
+ "e": {
+ "0.05": 0.0517855,
+ "0.15": 0.0825411,
+ "0.25": 0.100565,
+ "0.35": 0.114409,
+ "0.45": 0.125945,
+ "0.55": 0.135239,
+ "0.65": 0.136956,
+ "0.75": 0.134167,
+ "0.85": 0.0889851,
+ "0.95": 0.0294067,
+ },
+ },
+ "5.75": {
+ "periodfrac": 0.00887353,
+ "P_bin": 0.802745,
+ "normed_bin_frac_p_dist": 0.0716,
+ "normed_tripquad_frac_p_dist": 0.1336,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.445437,
+ "0.95": 0.554563,
+ },
+ "e": {
+ "0.05": 0.0502699,
+ "0.15": 0.0814303,
+ "0.25": 0.099944,
+ "0.35": 0.114252,
+ "0.45": 0.126226,
+ "0.55": 0.135904,
+ "0.65": 0.137694,
+ "0.75": 0.134937,
+ "0.85": 0.0896122,
+ "0.95": 0.0297304,
+ },
+ },
+ "6.25": {
+ "periodfrac": 0.00688391,
+ "P_bin": 0.76896,
+ "normed_bin_frac_p_dist": 0.05321,
+ "normed_tripquad_frac_p_dist": 0.1214,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.450046,
+ "0.95": 0.549954,
+ },
+ "e": {
+ "0.05": 0.0490604,
+ "0.15": 0.0805314,
+ "0.25": 0.0994411,
+ "0.35": 0.114131,
+ "0.45": 0.126467,
+ "0.55": 0.136463,
+ "0.65": 0.138314,
+ "0.75": 0.135567,
+ "0.85": 0.0900846,
+ "0.95": 0.0299411,
+ },
+ },
+ "6.75": {
+ "periodfrac": 0.00509626,
+ "P_bin": 0.733351,
+ "normed_bin_frac_p_dist": 0.03757,
+ "normed_tripquad_frac_p_dist": 0.1037,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
+ "0.45": 0.0,
+ "0.55": 0.0,
+ "0.65": 0.0,
+ "0.75": 0.0,
+ "0.85": 0.455221,
+ "0.95": 0.544779,
+ },
+ "e": {
+ "0.05": 0.0480717,
+ "0.15": 0.0797869,
+ "0.25": 0.0990233,
+ "0.35": 0.114031,
+ "0.45": 0.126672,
+ "0.55": 0.136936,
+ "0.65": 0.138838,
+ "0.75": 0.136092,
+ "0.85": 0.0904586,
+ "0.95": 0.0300905,
+ },
+ },
+ "7.25": {
+ "periodfrac": 0.00367211,
+ "P_bin": 0.69591,
+ "normed_bin_frac_p_dist": 0.02569,
+ "normed_tripquad_frac_p_dist": 0.0852,
+ "q": {
+ "0.15": 0.0,
+ "0.25": 0.0,
+ "0.35": 0.0,
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+ },
+ },
+ },
+ }
+ ],
+}
diff --git a/binarycpython/utils/plot_functions.py b/binarycpython/utils/plot_functions.py
index 16a1003333e234866eba4b76ef7c96a71ff18253..e22812007182a9f9454b9c4fa738620ac822b16a 100644
--- a/binarycpython/utils/plot_functions.py
+++ b/binarycpython/utils/plot_functions.py
@@ -39,7 +39,7 @@ from binarycpython.utils.custom_logging_functions import binary_c_log_code
# These are kept to the minimum necessary for each plotting routine.
CUSTOM_LOGGING_STRING_MASSES = """
-Printf("MASS_PLOTTING %30.12e %g %g %g %g\\n",
+Printf("MASS_PLOTTING %30.12e %g %g %g %g %g %g\\n",
//
stardata->model.time, // 1
@@ -48,8 +48,10 @@ Printf("MASS_PLOTTING %30.12e %g %g %g %g\\n",
stardata->common.zero_age.mass[1], //
stardata->star[0].mass,
- stardata->star[1].mass
- );
+ stardata->star[1].mass,
+ stardata->star[0].core_mass[ID_core(stardata->star[0].stellar_type)],
+ stardata->star[1].core_mass[ID_core(stardata->star[1].stellar_type)]
+);
"""
CUSTOM_LOGGING_STRING_ORBIT = """
@@ -341,7 +343,7 @@ def plot_masses(df, show_stellar_types: bool = False, show_plot: bool = True):
)
# Mass 1
- fig.axes[0].plot(df["time"], df["mass_1"], label="star 1")
+ fig.axes[0].plot(df["time"], df["mass_1"], label="star 1", color='red')
fig.axes[0].axhline(
df["pms_mass_1"].values.tolist()[0],
color="red",
@@ -350,17 +352,33 @@ def plot_masses(df, show_stellar_types: bool = False, show_plot: bool = True):
label="Initial mass 1",
alpha=0.5,
)
+ fig.axes[0].plot(
+ df["time"], df["core_mass_1"],
+ color="red",
+ linestyle="-.",
+ linewidth=2,
+ label="Core mass 1",
+ alpha=0.5,
+ )
# mass 2
fig.axes[0].plot(df["time"], df["mass_2"], color="orange", label="star 2")
fig.axes[0].axhline(
df["pms_mass_2"].values.tolist()[0],
- color="red",
+ color="orange",
linestyle="--",
linewidth=2,
label="Initial mass 2",
alpha=0.5,
)
+ fig.axes[0].plot(
+ df["time"], df["core_mass_2"],
+ color="orange",
+ linestyle="-.",
+ linewidth=2,
+ label="Core mass 2",
+ alpha=0.5,
+ )
# Make up
fig.axes[0].set_title("Stellar mass evolution")
@@ -484,6 +502,8 @@ def parse_function_masses(output: str):
"pms_mass_2",
"mass_1",
"mass_2",
+ "core_mass_1",
+ "core_mass_2",
]
# Go over the output.
diff --git a/binarycpython/utils/useful_funcs.py b/binarycpython/utils/useful_funcs.py
index 419407b66ac796850aff0d428ac7540f58d78b6f..8e990105375792798c3fdb67a47e2a366e654a39 100644
--- a/binarycpython/utils/useful_funcs.py
+++ b/binarycpython/utils/useful_funcs.py
@@ -13,6 +13,7 @@ Functions:
Tasks:
- TODO: check whether these functions are correct
+ - TODO: add unittest for maximum_mass_ratio_for_RLOF
"""
import math
@@ -26,8 +27,6 @@ AURSUN = 2.150445198804013386961742071435e02
YEARDY = 3.651995478818308811241877265275e02
-
-
def minimum_period_for_RLOF(M1, M2, metallicity, store_memaddr=-1):
"""
Wrapper function for _binary_c_bindings.return_minimum_orbit_for_RLOF
@@ -44,19 +43,27 @@ def minimum_period_for_RLOF(M1, M2, metallicity, store_memaddr=-1):
"""
- bse_dict = {"M_1": M1, "M_2": M2, "metallicity": metallicity, "minimum_orbital_period_for_instant_RLOF": 1, "minimum_separation_for_instant_RLOF": 1}
+ bse_dict = {
+ "M_1": M1,
+ "M_2": M2,
+ "metallicity": metallicity,
+ "minimum_orbital_period_for_instant_RLOF": 1,
+ "minimum_separation_for_instant_RLOF": 1,
+ }
argstring = "binary_c " + create_arg_string(bse_dict)
output = _binary_c_bindings.return_minimum_orbit_for_RLOF(argstring, store_memaddr)
- minimum_period = float(re.search('MINIMUM PERIOD (.*)', output).group(1))
+ minimum_period = float(re.search("MINIMUM PERIOD (.*)", output).group(1))
return minimum_period
+
# print(minimum_period_for_RLOF(10, 5, 0.02))
# print(minimum_period_for_RLOF(10, 2, 0.02))
+
def minimum_separation_for_RLOF(M1, M2, metallicity, store_memaddr=-1):
"""
Wrapper function for _binary_c_bindings.return_minimum_orbit_for_RLOF
@@ -73,19 +80,73 @@ def minimum_separation_for_RLOF(M1, M2, metallicity, store_memaddr=-1):
"""
- bse_dict = {"M_1": M1, "M_2": M2, "metallicity": metallicity, "minimum_orbital_period_for_instant_RLOF": 1, "minimum_separation_for_instant_RLOF": 1}
+ bse_dict = {
+ "M_1": M1,
+ "M_2": M2,
+ "metallicity": metallicity,
+ "minimum_orbital_period_for_instant_RLOF": 1,
+ "minimum_separation_for_instant_RLOF": 1,
+ }
argstring = "binary_c " + create_arg_string(bse_dict)
output = _binary_c_bindings.return_minimum_orbit_for_RLOF(argstring, store_memaddr)
- minimum_separation = float(re.search('MINIMUM SEPARATION (.*)', output).group(1))
+ minimum_separation = float(re.search("MINIMUM SEPARATION (.*)", output).group(1))
return minimum_separation
+
# print(minimum_separation_for_RLOF(0.08, 0.08, 0.00002))
# print(minimum_separation_for_RLOF(10, 2, 0.02))
+
+def maximum_mass_ratio_for_RLOF(
+ M1, orbital_period, metallicity=0.02, store_memaddr=None
+):
+ """
+ Wrapper function for _binary_c_bindings.return_maximum_mass_ratio_for_RLOF
+
+ Handles the output and returns the maximum mass ratio at which RLOF just does not occur at ZAMS
+
+ Args:
+ M1: Primary mass in solar mass
+ orbital_period: orbital period in days
+ metallicity: metallicity
+ store_memaddr (optional): store memory adress
+ Returns:
+ maximum mass ratio that just does not cause a RLOF at ZAMS
+ """
+
+ # Convert to orbital period in years
+ orbital_period = orbital_period / 3.651995478818308811241877265275e02
+
+ bse_dict = {
+ "M_1": M1,
+ "M_2": 0.01,
+ "separation": 0,
+ "orbital_period": orbital_period,
+ "metallicity": metallicity,
+ "maximum_mass_ratio_for_instant_RLOF": 1,
+ }
+
+ argstring = "binary_c " + create_arg_string(bse_dict)
+ output = _binary_c_bindings.return_maximum_mass_ratio_for_RLOF(
+ argstring, store_memaddr
+ )
+ stripped = output.strip()
+
+ if stripped == "NO MAXIMUM MASS RATIO < 1":
+ maximum_mass_ratio = 1
+ else:
+ maximum_mass_ratio = float(stripped.split()[-1])
+ return maximum_mass_ratio
+
+
+# print(maximum_mass_ratio_for_RLOF(4, 0.1, 0.002))
+# print(maximum_mass_ratio_for_RLOF(4, 1, 0.002))
+
+
def calc_period_from_sep(
M1: Union[int, float], M2: Union[int, float], sep: Union[int, float]
) -> Union[int, float]:
diff --git a/examples/example_population.py b/examples/example_population.py
index 7f5f776eee896f012577ea122d418eca235d4932..139531060c9145823d130bdfa1d793ca7830e604 100644
--- a/examples/example_population.py
+++ b/examples/example_population.py
@@ -1,9 +1,13 @@
-import os
-# import json
-# import time
+
+import os
from binarycpython.utils.grid import Population
-from binarycpython.utils.functions import get_help_all, get_help, create_hdf5, output_lines
+from binarycpython.utils.functions import (
+ get_help_all,
+ get_help,
+ create_hdf5,
+ output_lines,
+)
from binarycpython.utils.custom_logging_functions import temp_dir
#########################################################
@@ -11,6 +15,7 @@ from binarycpython.utils.custom_logging_functions import temp_dir
# The use of help(<function>) is a good way to inspect what parameters are there to use
#########################################################
+
def parse_function(self, output):
# EXAMPLE PARSE_FUNCTION
@@ -39,7 +44,7 @@ def parse_function(self, output):
values = el.split()[1:]
print(values)
- if not len(parameters)==len(values):
+ if not len(parameters) == len(values):
print("Amount of column names isnt equal to amount of columns")
raise ValueError
@@ -50,6 +55,7 @@ def parse_function(self, output):
with open(outfilename, "a") as f:
f.write(seperator.join(values) + "\n")
+
# Create population object
example_pop = Population()
@@ -67,14 +73,11 @@ example_pop.set(
orbital_period=45000000080, # bse_options
max_evolution_time=15000, # bse_options
eccentricity=0.02, # bse_options
-
# Set companion to low mass
- M_2=0.08, # Since in the example we run a single system, we should set the companion mass here. If we donm't do this, the code will complain.
-
+ M_2=0.08, # Since in the example we run a single system, we should set the companion mass here. If we donm't do this, the code will complain.
# grid_options
amt_cores=2, # grid_options
verbose=1, # verbosity. Not fully configured correctly yet but having it value of 1 prints alot of stuff
-
# Custom options # TODO: need to be set in grid_options probably
data_dir=os.path.join(
temp_dir(), "example_python_population_result"
@@ -122,11 +125,7 @@ if(stardata->star[0].stellar_type >= 13)
)
# Add grid variables
-resolution = {
- "M_1": 20,
- 'q': 20,
- 'per': 40
-}
+resolution = {"M_1": 20, "q": 20, "per": 40}
# Mass
example_pop.add_grid_variable(
@@ -153,27 +152,26 @@ example_pop.add_grid_variable(
# 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
+# condition="", # Impose a condition on this grid variable. Mostly for a check for yourself
# )
-# #
+# #
# test_pop.add_grid_variable(
# name="log10per", # in days
-# longname="log10(Orbital_Period)",
+# longname="log10(Orbital_Period)",
# valuerange=[0.15, 5.5],
# resolution="{}".format(resolution["per"]),
# spacingfunc="const(0.15, 5.5, {})".format(resolution["per"]),
# precode="""orbital_period = 10** log10per
# sep = calc_sep_from_period(M_1, M_2, orbital_period)
# sep_min = calc_sep_from_period(M_1, M_2, 10**0.15)
-# sep_max = calc_sep_from_period(M_1, M_2, 10**5.5)""",
+# sep_max = calc_sep_from_period(M_1, M_2, 10**5.5)""",
# probdist="sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**5.5), -0.55)",
# parameter_name="orbital_period",
# dphasevol="dlog10per",
# )
-
# Exporting of all the settings can be done with .export_all_info()
# on default it exports everything, but can be supressed by turning it off:
# population settings (bse_options, grid_options, custom_options), turn off with include_population
@@ -195,7 +193,7 @@ example_pop.export_all_info()
## Executing a population
## This uses the values generated by the grid_variables
-example_pop.evolve() # TODO: update this function call
+example_pop.evolve() # TODO: update this function call
# Wrapping up the results to an hdf5 file can be done by using the create_hdf5
# (<directory containing data and settings>) This function takes the settings file
diff --git a/include/binary_c_python.h b/include/binary_c_python.h
index 8d0cfec8830913310532957473232b646f9a5e08..be717c3b993eb85baac6bf7b7c28a6eecb819432 100644
--- a/include/binary_c_python.h
+++ b/include/binary_c_python.h
@@ -46,6 +46,11 @@ int return_minimum_orbit_for_RLOF(char * argstring,
char ** const error_buffer,
size_t * const nbytes);
+int return_maximum_mass_ratio_for_RLOF(char * argstring,
+ struct libbinary_c_store_t * store,
+ char ** buffer,
+ char ** error_buffer,
+ size_t * nbytes);
/* =================================================================== */
/* Functions to handle memory */
diff --git a/setup.py b/setup.py
index dd9f9fa56e8718f71223b6f2a57c58811e677ac1..3c86dc24e56be09981ad50312b50f7d38a0713f3 100644
--- a/setup.py
+++ b/setup.py
@@ -210,7 +210,7 @@ BINARY_C_PYTHON_API_MODULE = Extension(
extra_compile_args=[],
language="C",
)
-
+headers = ["src/includes/header.h"]
############################################################
# Making the extension function
############################################################
@@ -259,6 +259,8 @@ setup(
"pandas",
"astropy",
"matplotlib",
+ "py_rinterpolate",
+ "setproctitle",
],
include_package_data=True,
ext_modules=[BINARY_C_PYTHON_API_MODULE], # binary_c must be loaded
diff --git a/src/binary_c_python.c b/src/binary_c_python.c
index 71b549488c2104bdc513a4de5c8e7fd69060ee4e..c4df7d76b989aad2235b557844cd11c33dff3755 100644
--- a/src/binary_c_python.c
+++ b/src/binary_c_python.c
@@ -84,6 +84,8 @@ static char return_version_info_docstring[] =
"Return the version information of the used binary_c build";
static char return_minimum_orbit_for_RLOF_docstring[] =
"Returns a string containing the minimum orbit and separation for which a binary system does not RLOF at zams. Please use the wrapper functions in utils for this except when you know what you're doing";
+static char return_maximum_mass_ratio_for_RLOF_docstring[] =
+ "Returns a string containing the maximum mass ratio for which a binary system does not RLOF at zams. Optionally accepts a store_capsule. Please use the wrapper functions in utils for this except when you know what you're doing";
// other functionality
static char return_store_memaddr_docstring[] =
@@ -109,6 +111,7 @@ static PyObject* python_return_help_info(PyObject *self, PyObject *args);
static PyObject* python_return_help_all_info(PyObject *self, PyObject *args);
static PyObject* python_return_version_info(PyObject *self, PyObject *args);
static PyObject* python_return_minimum_orbit_for_RLOF(PyObject *self, PyObject *args);
+static PyObject* python_return_maximum_mass_ratio_for_RLOF(PyObject *self, PyObject *args, PyObject *kwargs);
// Other function headers
static PyObject* python_return_store_memaddr(PyObject *self, PyObject *args);
@@ -119,8 +122,6 @@ static PyObject* python_free_persistent_data_memaddr_and_return_json_output(PyOb
static PyObject* python_free_store_memaddr(PyObject *self, PyObject *args);
static PyObject* python_test_func(PyObject *self, PyObject *args);
-
-
/* Set the module functions */
static PyMethodDef module_methods[] = {
// Wierdly, this casting to a PyCFunction, which usually takes only 2 args, now works when giving keywords. See https://stackoverflow.com/q/10264080
@@ -132,6 +133,7 @@ static PyMethodDef module_methods[] = {
{"return_help_all", python_return_help_all_info, METH_VARARGS, return_help_all_info_docstring},
{"return_version_info", python_return_version_info, METH_VARARGS, return_version_info_docstring},
{"return_minimum_orbit_for_RLOF", python_return_minimum_orbit_for_RLOF, METH_VARARGS, return_minimum_orbit_for_RLOF_docstring},
+ {"return_maximum_mass_ratio_for_RLOF", (PyCFunction)python_return_maximum_mass_ratio_for_RLOF, METH_VARARGS|METH_KEYWORDS, return_maximum_mass_ratio_for_RLOF_docstring},
// memory
{"return_store_memaddr", python_return_store_memaddr, METH_VARARGS, return_store_memaddr_docstring},
@@ -427,6 +429,124 @@ static PyObject* python_return_minimum_orbit_for_RLOF(PyObject *self, PyObject *
return return_string;
}
+static PyObject* python_return_maximum_mass_ratio_for_RLOF(PyObject *self, PyObject *args, PyObject *kwargs)
+{
+ static char* keywords[] = {"argstring", "store_capsule", NULL};
+
+ /* set vars and default values for some*/
+ char *argstring;
+ PyObject * store_capsule = NULL;
+
+ /* Parse the input tuple */
+ // By using the keywords argument it scans over the given set of kwargs, but if they are not given then the default value is used
+ if(!PyArg_ParseTupleAndKeywords(args, kwargs, "s|O", keywords, &argstring, &store_capsule))
+ {
+ return NULL;
+ }
+
+ // Store
+ struct libbinary_c_store_t * store = NULL;
+ if (store_capsule != NULL)
+ {
+ if (PyCapsule_IsValid(store_capsule, "STORE"))
+ {
+ if (!(store = (struct libbinary_c_store_t *) PyCapsule_GetPointer(store_capsule, "STORE")))
+ return NULL;
+ debug_printf("Unpacked store pointer %p from capsule\n", store_capsule);
+ }
+ }
+
+ // Setup buffers
+ char * buffer;
+ char * error_buffer;
+ size_t nbytes;
+
+ /* Call c-function */
+ int out MAYBE_UNUSED = return_maximum_mass_ratio_for_RLOF(
+ argstring, // String containing the arguments for the system
+ store, // value for store memaddr
+ &buffer,
+ &error_buffer,
+ &nbytes);
+
+ /* copy the buffer to a python string */
+ PyObject * return_string = Py_BuildValue("s", buffer);
+ PyObject * return_error_string MAYBE_UNUSED = Py_BuildValue("s", error_buffer);
+
+ if(error_buffer != NULL && strlen(error_buffer)>0)
+ {
+ fprintf(stderr,
+ "Error (in function: python_return_maximum_mass_ratio_for_RLOF): %s\n",
+ error_buffer);
+ }
+
+ Safe_free(buffer);
+ Safe_free(error_buffer);
+
+ return return_string;
+}
+
+
+int return_maximum_mass_ratio_for_RLOF(char * argstring,
+ struct libbinary_c_store_t * store,
+ char ** buffer,
+ char ** error_buffer,
+ size_t * nbytes)
+{
+ /*
+ * Return the maximum mass ratio for RLOF given M1 and period
+ * If a valid store is passed in then we use it. otherwise a new store is made and released
+ */
+ struct libbinary_c_stardata_t *stardata = NULL;
+
+ /* Determine whether to free the store memory adress*/
+ Boolean free_store = FALSE;
+ if (store==NULL)
+ {
+ debug_printf("Decided to free the store memaddr\n");
+ free_store = TRUE;
+ }
+
+ binary_c_new_system(&stardata,
+ NULL,
+ NULL,
+ &store,
+ NULL,
+ &argstring,
+ -1);
+
+ // Set preferences.
+ stardata->preferences->show_maximum_mass_ratio_for_instant_RLOF = TRUE;
+ snprintf(stardata->preferences->log_filename,
+ STRING_LENGTH-1,"%s","/dev/null");
+ snprintf(stardata->preferences->api_log_filename_prefix,
+ STRING_LENGTH-1,"%s","/dev/null");
+ stardata->preferences->internal_buffering = INTERNAL_BUFFERING_STORE;
+ stardata->preferences->batchmode = BATCHMODE_LIBRARY;
+
+ /* Actually show the instant_rlof */
+ binary_c_show_instant_RLOF(stardata); // prints to the buffer.
+
+ /* put results in buffer */
+ binary_c_buffer_info(stardata, buffer, nbytes);
+
+ /* Put errors in error buffer */
+ binary_c_error_buffer(stardata, error_buffer);
+
+ Boolean free_persistent_data = TRUE;
+
+ /* free stardata (except the buffer) */
+ binary_c_free_memory(&stardata, // Stardata
+ TRUE, // free_preferences
+ TRUE, // free_stardata
+ free_store, // free_store
+ FALSE, // free_raw_buffer TODO: fix this
+ free_persistent_data // free_persistent
+ );
+
+ return 0;
+}
+
/* ============================================================================== */
/* Wrappers to functions that call other functionality */
/* ============================================================================== */
@@ -669,6 +789,22 @@ int run_system(char * argstring,
// store can be NULL, but could be a valid pointer to a store
// persistent_data can be NULL, but could be a valid pointer to a persistent_data
+ /* Determine whether to free the store memory adress*/
+ Boolean free_store = FALSE;
+ if (store==NULL)
+ {
+ debug_printf("Decided to free the store memaddr\n");
+ free_store = TRUE;
+ }
+
+ /* Determine whether to free the persistent data memory adress*/
+ Boolean free_persistent_data = FALSE;
+ if (persistent_data == NULL)
+ {
+ debug_printf("Decided to free the persistent_data memaddr\n");
+ free_persistent_data = TRUE;
+ }
+
/* Set up new system */
binary_c_new_system(&stardata, // stardata
NULL, // previous_stardatas
@@ -715,22 +851,6 @@ int run_system(char * argstring,
/* get error buffer pointer */
binary_c_error_buffer(stardata, error_buffer);
- /* Determine whether to free the store memory adress*/
- Boolean free_store = FALSE;
- if (store == NULL)
- {
- debug_printf("Decided to free the store memaddr\n");
- free_store = TRUE;
- }
-
- /* Determine whether to free the persistent data memory adress*/
- Boolean free_persistent_data = FALSE;
- if (persistent_data == NULL)
- {
- debug_printf("Decided to free the persistent_data memaddr\n");
- free_persistent_data = TRUE;
- }
-
/* free stardata (except the buffer) */
binary_c_free_memory(&stardata, // Stardata
TRUE, // free_preferences
@@ -989,7 +1109,7 @@ int return_minimum_orbit_for_RLOF(char * argstring,
binary_c_error_buffer(stardata, error_buffer);
/* Determine whether to free the store memory adress*/
- Boolean free_store = FALSE;
+ Boolean free_store = FALSE; // TODO: fix this. Its using capsules now
if (store_memaddr == -1)
{
debug_printf("Decided to free the store memaddr\n");