Revert updates

lppls/lppls.py

@@ -485,7 +485,7 @@ def compute_nested_fits(
             i_idx += 1
             for j in range(0, window_delta, inner_increment):
                 obs_shrinking_slice = obs[:, j:window_size]
-                tc, m, w, a, b, c, c1, c2, O, D = self.fit(
+                tc, m, w, a, b, c, _, _, _, _ = self.fit(
                     max_searches, obs=obs_shrinking_slice
                 )
                 res[i_idx - 1].append([])
@@ -581,8 +581,8 @@ def _func_compute_nested_fits(self, args):
                     # "t2_d": self.ordinal_to_date(nested_t2),
                     "t1": nested_t1,
                     "t2": nested_t2,
-                    # "O": O,
-                    # "D": D,
+                    "O": O,
+                    "D": D,
                 }
             )
 
@@ -614,8 +614,7 @@ def _is_D_in_range(self, m, w, b, c, D_min):
         return False if m <= 0 or w <= 0 else abs((m * b) / (w * c)) > D_min
 
     def get_oscillations(self, w, tc, t1, t2):
-        dt = np.abs(tc - t2) + 1e-8
-        return (w / (2.0 * np.pi)) * np.log((tc - t1) / dt)
+        return (w / (2.0 * np.pi)) * np.log((tc - t1) / (tc - t2))
 
     def get_damping(self, m, w, b, c):
         return (m * np.abs(b)) / (w * np.abs(c))

lppls/tests/test_lppls.py

@@ -94,7 +94,7 @@ def test_mp_compute_nested_fits(observations, lppls_model):
     assert res[0]['t1'] == 0.0
     assert res[0]['t2'] == 79.0
     assert res[4]['t1'] == 20.0
-    expected_keys = {'tc_d', 'tc', 'm', 'w', 'a', 'b', 'c', 'c1', 'c2', 't1_d', 't2_d', 't1', 't2', 'O', 'D'}
+    expected_keys = {'tc', 'm', 'w', 'a', 'b', 'c', 'c1', 'c2', 't1', 't2', 'O', 'D'}
     assert len(res[0]['res']) == 30
     assert set(res[0]['res'][0]).issubset(expected_keys)
 

setup.py

@@ -4,7 +4,7 @@
     long_description = fh.read()
 
 setuptools.setup(name='lppls',
-      version='0.6.16',
+      version='0.6.18',
       description='A Python module for fitting the LPPLS model to data.',
       packages=['lppls'],
       author='Josh Nielsen',