remove O D from compute nested fits.

lppls/lppls.py

@@ -146,9 +146,9 @@ def fit(self, max_searches, minimizer="Nelder-Mead", obs=None):
             # Increment search count on SVD convergence error, but raise all other exceptions.
             try:
                 tc, m, w, a, b, c, c1, c2 = self.estimate_params(obs, seed, minimizer)
-                O = self.get_oscillations(w, tc, t1, t2)
-                D = self.get_damping(m, w, b, c)
-                return tc, m, w, a, b, c, c1, c2, O, D
+                # O = self.get_oscillations(w, tc, t1, t2)
+                # D = self.get_damping(m, w, b, c)
+                return tc, m, w, a, b, c, c1, c2, #O, D
             except Exception as e:
                 # print(e)
                 search_count += 1
@@ -270,8 +270,8 @@ def compute_indicators(self, res, filter_conditions_config=None):
                 w = fits["w"]
                 b = fits["b"]
                 c = fits["c"]
-                O = fits["O"]
-                D = fits["D"]
+                # O = fits["O"]
+                # D = fits["D"]
 
                 # t_delta = t2 - t1
                 # pct_delta_min = t_delta * 0.5
@@ -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, c1, c2 = self.fit(
                     max_searches, obs=obs_shrinking_slice
                 )
                 res[i_idx - 1].append([])

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'}
     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='0.6.17',
       description='A Python module for fitting the LPPLS model to data.',
       packages=['lppls'],
       author='Josh Nielsen',