diff --git a/badges/test_coverage.svg b/badges/test_coverage.svg
index c97e50489fae4a7075aff56a73e3e1936d1f76f1..dfb99a19bbb554f31bc566f509dce7df5d8bb40d 100644
--- a/badges/test_coverage.svg
+++ b/badges/test_coverage.svg
@@ -15,7 +15,7 @@
<g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
<text x="31.5" y="15" fill="#010101" fill-opacity=".3">coverage</text>
<text x="31.5" y="14">coverage</text>
- <text x="80" y="15" fill="#010101" fill-opacity=".3">66%</text>
- <text x="80" y="14">66%</text>
+ <text x="80" y="15" fill="#010101" fill-opacity=".3">67%</text>
+ <text x="80" y="14">67%</text>
</g>
</svg>
diff --git a/binarycpython/tests/test_custom_logging.py b/binarycpython/tests/test_custom_logging.py
index 329dd7da59fc57aa524d74de9683f32f561d4f7c..55c618c67c58cfa4079c74d1d36355014772ebe3 100644
--- a/binarycpython/tests/test_custom_logging.py
+++ b/binarycpython/tests/test_custom_logging.py
@@ -65,20 +65,21 @@ class test_binary_c_log_code(unittest.TestCase):
def test_binary_c_log_code(self):
with Capturing() as _:
self._test_binary_c_log_code()
- # print("\n".join(output))
def _test_binary_c_log_code(self):
"""
Test to see if passing a print statement to the function results in correct binary_c output
"""
+ # binary_c should not compile anything here
input_1 = "None"
output_1 = binary_c_log_code(input_1, verbosity=1)
self.assertEqual(output_1, None, msg="Output should be None")
+ #
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, verbosity=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\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,
@@ -153,7 +154,7 @@ class test_from_binary_c_config(unittest.TestCase):
output_2 = from_binary_c_config(BINARY_C_CONFIG, input_2)
self.assertIn(
output_2,
- ["2.1.7", "2.2pre1", "2.2.0", "2.2.1"],
+ ["2.1.7", "2.2pre1", "2.2.0", "2.2.1", "2.2.2"],
msg="binary_c version doesnt match",
)
@@ -213,4 +214,5 @@ class test_create_and_load_logging_function(unittest.TestCase):
if __name__ == "__main__":
- unittest.main()
+ # unittest.main()
+ test_binary_c_log_code().test_binary_c_log_code()
diff --git a/binarycpython/tests/test_grid.py b/binarycpython/tests/test_grid.py
index 97afaec8c9032458be7f0d2eff2f9a71ceb6a40c..413f8189cfe092ac57ce41c0ae729989c7c4888e 100644
--- a/binarycpython/tests/test_grid.py
+++ b/binarycpython/tests/test_grid.py
@@ -110,11 +110,11 @@ class test__setup(unittest.TestCase):
Unittests for _setup function
"""
- def test_setup(self):
- with Capturing() as _:
- self._test_setup()
+ # def test_setup(self):
+ # with Capturing() as _:
+ # self._test_setup()
- def _test_setup(self):
+ def test_setup(self):
"""
Unittests for function _setup
"""
@@ -124,7 +124,7 @@ class test__setup(unittest.TestCase):
self.assertTrue("metallicity" in test_pop.defaults)
self.assertNotIn("help_all", test_pop.cleaned_up_defaults)
self.assertEqual(test_pop.bse_options, {})
- self.assertEqual(test_pop.custom_options, {})
+ self.assertEqual(test_pop.custom_options, {"save_snapshot": False})
self.assertEqual(test_pop.argline_dict, {})
self.assertEqual(test_pop.persistent_data_memory_dict, {})
self.assertTrue(test_pop.grid_options["parse_function"] is None)
@@ -617,6 +617,7 @@ class test_resultdict(unittest.TestCase):
dphasevol="dlnm1",
parameter_name="M_1",
condition="", # Impose a condition on this grid variable. Mostly for a check for yourself
+ gridtype="left",
)
## Executing a population
@@ -775,7 +776,7 @@ class test_grid_evolve(unittest.TestCase):
parse_function=parse_function_test_grid_evolve_2_threads_with_custom_logging,
)
test_pop.set(ensemble=0)
- resolution = {"M_1": 2}
+ resolution = {"M_1": 20}
test_pop.add_grid_variable(
name="lnm1",
diff --git a/binarycpython/utils/population_extensions/distribution_functions.py b/binarycpython/utils/population_extensions/distribution_functions.py
index 67535d8424d75f74d5636c40d4f54eec5d7e80b8..5dedd62a81e15504855b3bdda503f3f348c1aded 100644
--- a/binarycpython/utils/population_extensions/distribution_functions.py
+++ b/binarycpython/utils/population_extensions/distribution_functions.py
@@ -302,7 +302,9 @@ class distribution_functions:
m0, m1, m2, m_max, p1, p2, p3
)
- if m < m0:
+ if (
+ m <= m0
+ ): # TODO: this needs to be checked! Compare two choices in this with the same sampling ranges.
prob = 0.0 # Below lower bound
elif m <= m1:
prob = three_part_powerlaw_constants[0] * (m**p1) # Between m0 and m1
@@ -759,7 +761,7 @@ class distribution_functions:
log10Pmin = max(-1.0, log10Pmin)
# save mass input and limit mass used (M1 from now on) to fitted range
- Mwas = M1
+ # Mwas = M1 TODO: why is this here?
M1 = max(1.15, min(16.3, M1))
# Calculate the normalisations
@@ -921,7 +923,7 @@ class distribution_functions:
The multiplicity fractions of the quadruples are folded onto that of the triples
"""
- if not max_multiplicity in range(1, 5):
+ if max_multiplicity not in range(1, 5):
msg = "\tMoe and di Stefano 2017: merge_multiplicities: max_multiplicity has to be between 1 and 4. It is {} now".format(
max_multiplicity
)
@@ -1422,24 +1424,26 @@ class distribution_functions:
)
# now we integrate and renormalise (if the table is not all zero)
- I = self.get_integration_constant_q(
+ integration_constant_q = self.get_integration_constant_q(
q_interpolator, tmp_table, qdata, verbosity=verbosity
)
- if I > 0:
+ if integration_constant_q > 0:
# normalise to 1.0 by dividing the data by 1.0/$I
- q_interpolator.multiply_table_column(1, 1.0 / I)
+ q_interpolator.multiply_table_column(
+ 1, 1.0 / integration_constant_q
+ )
# test this
- new_I = self.get_integration_constant_q(
+ new_integration_constant_q = self.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:
+ if abs(1.0 - new_integration_constant_q) > 1e-6:
verbose_print(
"\tMoe and di Stefano 2017: build_q_table: Error: > 1e-6 in q probability integral: {}".format(
- I
+ integration_constant_q
),
verbosity,
_MOE2017_VERBOSITY_LEVEL,
@@ -1520,7 +1524,7 @@ class distribution_functions:
"""
dq = 1e-3 # resolution of the integration/renormalisation
- I = 0
+ integration_constant_q = 0
# integrate: note that the value of the integral is
# meaningless to within a factor (which depends on $dq)
@@ -1538,8 +1542,8 @@ class distribution_functions:
)
raise ValueError(msg)
- I += x[0] * dq
- return I
+ integration_constant_q += x[0] * dq
+ return integration_constant_q
def fill_data(self, sample_values, data_dict):
"""
@@ -1550,19 +1554,19 @@ class distribution_functions:
"""
data = {}
- I = 0
+ integration_constant = 0
- dstep = float(sample_values[1]) - float(sample_values[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
+ integration_constant += val
# Normalise the data
for sample_value in sample_values:
- data[sample_value] = data[sample_value] / I
+ data[sample_value] = data[sample_value] / integration_constant
return data
@@ -1579,6 +1583,8 @@ class distribution_functions:
Function to calculate the e integral
We need to renormalise this because min_per > 0, and not all periods should be included
+
+ TODO: create function ot actually do this in a more general way
"""
global Moecache
@@ -1595,7 +1601,7 @@ class distribution_functions:
# Check for cached value. If it doesn't exist: calculate
if not Moecache[integrals_string].get(mass_period_string, None):
- I = 0
+ integation_constant_e = 0
decc = 1e-3
for ecc in np.arange(min_ecc, max_ecc, decc):
@@ -1608,10 +1614,10 @@ class distribution_functions:
]
)[0]
- I += dp_decc * decc
+ integation_constant_e += dp_decc * decc
# Set the integral value in the dict
- Moecache[integrals_string][mass_period_string] = I
+ Moecache[integrals_string][mass_period_string] = integation_constant_e
verbose_print(
"\tMoe and di Stefano 2017: calc_ecc_integral: min_ecc: {} max ecc: {} integrals_string: {} interpolator_name: {} mass_string: {} period_string: {} mass: {} period: {} I: {}".format(
min_ecc,
@@ -1622,7 +1628,7 @@ class distribution_functions:
period_string,
options[mass_string],
options[period_string],
- I,
+ integation_constant_e,
),
verbosity,
_MOE2017_VERBOSITY_LEVEL,
@@ -1668,7 +1674,7 @@ class distribution_functions:
# Check for cached value. If it doesn't exist: calculate
if not Moecache[integrals_string].get(options[mass_string], None):
- I = 0
+ integation_constant_P = 0
dlogP = 1e-3
for logP in np.arange(min_logP, max_logP, dlogP):
@@ -1677,10 +1683,10 @@ class distribution_functions:
[np.log10(options[mass_string]), logP]
)[0]
- I += dp_dlogP * dlogP
+ integation_constant_P += dp_dlogP * dlogP
# Set the integral value in the dict
- Moecache[integrals_string][options[mass_string]] = I
+ Moecache[integrals_string][options[mass_string]] = integation_constant_P
verbose_print(
"\tMoe and di Stefano 2017: calc_P_integral: min_logP: {} integrals_string: {} interpolator_name: {} mass_string: {} mass: {} I: {}".format(
min_logP,
@@ -1688,7 +1694,7 @@ class distribution_functions:
interpolator_name,
mass_string,
options[mass_string],
- I,
+ integation_constant_P,
),
verbosity,
_MOE2017_VERBOSITY_LEVEL,
@@ -2142,11 +2148,11 @@ class distribution_functions:
# 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["M_1+M_2"], options["mmin"], max_sep3
- )
+ # # TODO: fix this here
+ # max_sep3 = 0.2 * options["sep2"] * (1.0 + options["ecc2"])
+ # max_per3 = calc_period_from_sep(
+ # options["M_1+M_2"], options["mmin"], max_sep3
+ # )
# Calculate P integral or use the cached value
# TODO: Make sure we use the correct period idea here.
diff --git a/pyproject.toml b/pyproject.toml
index 44a9414147e85013fb81672a5f4f9f0d3e192e09..e879add9c5115d905adb90543bc7e00c8c956fbe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,4 @@
[tool.black]
-
target-version = ['py39']
extend-exclude = '''
# A regex preceded with ^/ will apply only to files and directories
@@ -20,7 +19,10 @@ extend-exclude = '''
max-line-length = 89
max-complexity = 18
-
-[tool.coverage]
-#omit =
-# test_*.py
+#[tool.coverage]
+#[tool.coverage.run]
+#omit =[*/.local/*,
+## omit everything in /usr
+#/usr/*,
+## omit this single file
+#utils/tirefire.py]