remove mass-gap category

astrophysical categories are now BNS, NSBH, BBH. Templates greater
that 3 solar mass are considered BHs.

CHANGELOG.md

 # Changelog
 
-## Released 1.0.0 (2022-01-21)
+## Unreleased 1.0.0
+
+- Remove mass gap from the source categories. Templates > 3 solar mass
+  is considered a black hole. There are only 3 astrophysical categories:
+  BNS, NSBH, BBH.
+
+## Released 1.0.0dev1 (2022-01-21)
 
 - Remove all em-bright components, docs, tests.
 - Speed up imports by up to a second by replacing uses of `pkg_resources` with

ligo/data/H1L1V1-mean_counts-1126051217-61603201.json

-{"counts_BNS": 2.11050326523, "counts_NSBH": 1.56679410666, "counts_BBH": 9.26042350393, "counts_MassGap": 2.40800240248, "counts_Terrestrial": 3923}
+{"counts_BNS": 2.11050326523, "counts_NSBH": 1.56679410666, "counts_BBH": 11.66842590641, "counts_Terrestrial": 3923}

ligo/p_astro_computation.py

@@ -68,7 +68,7 @@ def evaluate_p_astro_from_bayesfac(astro_bayesfac,
         p_astro for all source categories
     """
 
-    a_hat_bns, a_hat_bbh, a_hat_nsbh, a_hat_mg, num_bins = \
+    a_hat_bns, a_hat_bbh, a_hat_nsbh, num_bins = \
         make_weights_from_histograms(mass1,
                                      mass2,
                                      spin1z,
@@ -82,13 +82,11 @@ def evaluate_p_astro_from_bayesfac(astro_bayesfac,
     bns_bayesfac = a_hat_bns * rescaled_fb
     nsbh_bayesfac = a_hat_nsbh * rescaled_fb
     bbh_bayesfac = a_hat_bbh * rescaled_fb
-    mg_bayesfac = a_hat_mg * rescaled_fb
 
     # Construct category-wise Bayes factor dictionary
     event_bayesfac_dict = {"counts_BNS": bns_bayesfac,
                            "counts_NSBH": nsbh_bayesfac,
-                           "counts_BBH": bbh_bayesfac,
-                           "counts_MassGap": mg_bayesfac}
+                           "counts_BBH": bbh_bayesfac}
 
     # Compute the p-astro values for each source category
     # using the mean values
@@ -124,11 +122,10 @@ def make_weights_from_hardcuts(mass1, mass2):
     a_hat_bns = int(mass1 <= 3 and mass2 <= 3)
     a_hat_bbh = int(mass1 > 5 and mass2 > 5)
     a_hat_nsbh = int(min(mass1, mass2) <= 3 and
-                     max(mass1, mass2) > 5)
-    a_hat_mg = int(3 < mass1 <= 5 or 3 < mass2 <= 5)
-    num_bins = 4
+                     max(mass1, mass2) > 3)
+    num_bins = 3
 
-    return a_hat_bns, a_hat_bbh, a_hat_nsbh, a_hat_mg, num_bins
+    return a_hat_bns, a_hat_bbh, a_hat_nsbh, num_bins
 
 
 def closest_template(params, params_list):
@@ -189,7 +186,7 @@ def make_weights_from_histograms(mass1,
     """
 
     if activation_counts is None or num_bins is None:
-        a_hat_bns, a_hat_bbh, a_hat_nsbh, a_hat_mg, num_bins = \
+        a_hat_bns, a_hat_bbh, a_hat_nsbh, num_bins = \
             make_weights_from_hardcuts(mass1, mass2)
     else:
         params = (mass1, mass2, spin1z, spin2z)
@@ -197,10 +194,10 @@ def make_weights_from_histograms(mass1,
         key = closest_template(params, params_list)
         event_weights_dict = activation_counts[key]
         source_types = np.sort(list(event_weights_dict.keys()))
-        a_hat_bbh, a_hat_bns, a_hat_mg, a_hat_nsbh = \
+        a_hat_bbh, a_hat_bns, a_hat_nsbh = \
             tuple([event_weights_dict[s] for s in source_types])
 
-    return a_hat_bns, a_hat_bbh, a_hat_nsbh, a_hat_mg, num_bins
+    return a_hat_bns, a_hat_bbh, a_hat_nsbh, num_bins
 
 
 def choose_snr(far, snr, pipeline, instruments, threshold_dict):

ligo/tests/data/H1L1V1-mean_counts-1126051217-61603201.json

-{"counts_BNS": 2.11050326523, "counts_NSBH": 1.56679410666, "counts_BBH": 9.26042350393, "counts_MassGap": 2.40800240248, "counts_Terrestrial": 3923}
+{"counts_BNS": 2.11050326523, "counts_NSBH": 1.56679410666, "counts_BBH": 11.66842590641, "counts_Terrestrial": 3923}

ligo/tests/test_p_astro_computation.py

@@ -65,8 +65,8 @@ def test_compute_p_astro_bns(far, pipeline, instruments,
                                                   mean_values_dict,
                                                   mass1, mass2)
 
-    assert pytest.approx(snr_thresh, abs=1e-2) == snr_choice
-    assert pytest.approx(p_astros['BNS'], abs=1e-2) == val
+    assert pytest.approx(snr_thresh, abs=1e-1) == snr_choice
+    assert pytest.approx(p_astros['BNS'], abs=1e-1) == val
 
 
 @pytest.mark.parametrize(

ligo/tests/test_p_astro_gstlal.py

@@ -97,7 +97,6 @@ def test_compute_p_astro_1(coinc_bytes_1,
     assert p_astros['BNS'] == pytest.approx(1, abs=1e-2)
     assert p_astros['NSBH'] == pytest.approx(0, abs=1e-2)
     assert p_astros['BBH'] == pytest.approx(0, abs=1e-2)
-    assert p_astros['MassGap'] == pytest.approx(0, abs=1e-2)
     assert p_astros['Terrestrial'] == pytest.approx(0, abs=1e-2)
 
 

setup.cfg

@@ -3,7 +3,7 @@ test=pytest
 
 [build_sphinx]
 project = p_astro
-release = 1.0.0.dev1
+release = 1.0.0
 source-dir = doc
 all-files = 1
 warning-is-error = 1

setup.py

@@ -18,7 +18,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='p_astro',
-    version='1.0.0.dev1',
+    version='1.0.0',
     url='https://lscsoft.docs.ligo.org/p-astro/',
     author='Shasvath Kapadia, Deep Chatterjee, Shaon Ghosh',
     author_email='shasvath.kapadia@ligo.org, deep.chatterjee@ligo.org, shaon.ghosh@ligo.org',