Update formatting.

PiperOrigin-RevId: 213963765

research/astronet/light_curve_util/median_filter_test.py

@@ -124,5 +124,5 @@ class MedianFilterTest(absltest.TestCase):
     np.testing.assert_array_equal([7, 1, 5, 2, 3], result)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
   absltest.main()

research/astronet/light_curve_util/util.py

@@ -138,8 +138,8 @@ def interpolate_missing_time(time, cadence_no=None, fill_value="extrapolate"):
     time: A numpy array of monotonically increasing values, with missing values
       denoted by NaN or Inf.
     cadence_no: Optional numpy array of cadence numbers corresponding to the
-        time values. If not provided, missing time values are assumed to be
-        evenly spaced between present time values.
+      time values. If not provided, missing time values are assumed to be evenly
+      spaced between present time values.
     fill_value: Specifies how missing time values should be treated at the
       beginning and end of the array. See scipy.interpolate.interp1d.
 
@@ -186,8 +186,8 @@ def interpolate_masked_spline(all_time, all_masked_time, all_masked_spline):
         points linearly interpolated.
   """
   interp_spline = []
-  for time, masked_time, masked_spline in zip(
-      all_time, all_masked_time, all_masked_spline):
+  for time, masked_time, masked_spline in zip(all_time, all_masked_time,
+                                              all_masked_spline):
     if masked_time.size:
       interp_spline.append(np.interp(time, masked_time, masked_spline))
     else:

research/astronet/third_party/kepler_spline/kepler_spline.py

@@ -124,8 +124,8 @@ class SplineMetadata(object):
     bad_bkspaces: List of break-point spacing values that failed.
     likelihood_term: The likelihood term of the Bayesian Information Criterion;
       -2*ln(L), where L is the likelihood of the data given the model.
-    penalty_term: The penalty term for the number of parameters in the
-        Bayesian Information Criterion.
+    penalty_term: The penalty term for the number of parameters in the Bayesian
+      Information Criterion.
     bic: The value of the Bayesian Information Criterion; equal to
       likelihood_term + penalty_coeff * penalty_term.
   """
@@ -165,12 +165,11 @@ def choose_kepler_spline(all_time,
     maxiter: Maximum number of attempts to fit each spline after removing badly
       fit points.
     penalty_coeff: Coefficient of the penalty term for using more parameters in
-        the Bayesian Information Criterion. Decreasing this value will allow
-        more parameters to be used (i.e. smaller break-point spacing), and
-        vice-versa.
+      the Bayesian Information Criterion. Decreasing this value will allow more
+      parameters to be used (i.e. smaller break-point spacing), and vice-versa.
     verbose: Whether to log individual spline errors. Note that if bkspaces
-        contains many values (particularly small ones) then this may cause
-        logging pollution if calling this function for many light curves.
+      contains many values (particularly small ones) then this may cause logging
+      pollution if calling this function for many light curves.
 
   Returns:
     spline: List of numpy arrays; values of the best-fit spline corresponding to
@@ -297,12 +296,11 @@ def fit_kepler_spline(all_time,
     maxiter: Maximum number of attempts to fit each spline after removing badly
       fit points.
     penalty_coeff: Coefficient of the penalty term for using more parameters in
-        the Bayesian Information Criterion. Decreasing this value will allow
-        more parameters to be used (i.e. smaller break-point spacing), and
-        vice-versa.
+      the Bayesian Information Criterion. Decreasing this value will allow more
+      parameters to be used (i.e. smaller break-point spacing), and vice-versa.
     verbose: Whether to log individual spline errors. Note that if bkspaces
-        contains many values (particularly small ones) then this may cause
-        logging pollution if calling this function for many light curves.
+      contains many values (particularly small ones) then this may cause logging
+      pollution if calling this function for many light curves.
 
   Returns:
     spline: List of numpy arrays; values of the best-fit spline corresponding to