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/periodic_event.py

@@ -62,7 +62,7 @@ class Event(object):
       other_event: An Event.
       period_rtol: Relative tolerance in matching the periods.
       t0_durations: Tolerance in matching the t0 values, in units of the other
-          Event's duration.
+        Event's duration.
 
     Returns:
       True if this Event is the same as other_event, within the given tolerance.

research/astronet/light_curve_util/util.py

@@ -51,9 +51,9 @@ def split(all_time, all_flux, gap_width=0.75):
 
   Args:
     all_time: Numpy array or sequence of numpy arrays; each is a sequence of
-        time values.
+      time values.
     all_flux: Numpy array or sequence of numpy arrays; each is a sequence of
-        flux values of the corresponding time array.
+      flux values of the corresponding time array.
     gap_width: Minimum gap size (in time units) for a split.
 
   Returns:
@@ -91,9 +91,9 @@ def remove_events(all_time,
 
   Args:
     all_time: Numpy array or sequence of numpy arrays; each is a sequence of
-        time values.
+      time values.
     all_flux: Numpy array or sequence of numpy arrays; each is a sequence of
-        flux values of the corresponding time array.
+      flux values of the corresponding time array.
     events: List of Event objects to remove.
     width_factor: Fractional multiplier of the duration of each event to remove.
     include_empty_segments: Whether to include empty segments in the output.
@@ -136,12 +136,12 @@ def interpolate_missing_time(time, cadence_no=None, fill_value="extrapolate"):
 
   Args:
     time: A numpy array of monotonically increasing values, with missing values
-        denoted by NaN or Inf.
+      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.
+      beginning and end of the array. See scipy.interpolate.interp1d.
 
   Returns:
     A numpy array of the same length as the input time array, with NaN/Inf
@@ -177,17 +177,17 @@ def interpolate_masked_spline(all_time, all_masked_time, all_masked_spline):
   Args:
     all_time: List of numpy arrays; each is a sequence of time values.
     all_masked_time: List of numpy arrays; each is a sequence of time values
-        with some values missing (masked).
+      with some values missing (masked).
     all_masked_spline: List of numpy arrays; the masked spline values
-        corresponding to all_masked_time.
+      corresponding to all_masked_time.
 
   Returns:
     interp_spline: List of numpy arrays; each is the masked spline with missing
         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

@@ -37,9 +37,9 @@ def kepler_spline(time, flux, bkspace=1.5, maxiter=5, outlier_cut=3):
     flux: Numpy array; the flux (brightness) values of the light curve.
     bkspace: Spline break point spacing in time units.
     maxiter: Maximum number of attempts to fit the spline after removing badly
-        fit points.
+      fit points.
     outlier_cut: The maximum number of standard deviations from the median
-        spline residual before a point is considered an outlier.
+      spline residual before a point is considered an outlier.
 
   Returns:
     spline: The values of the fitted spline corresponding to the input time
@@ -119,15 +119,15 @@ class SplineMetadata(object):
 
   Attributes:
     light_curve_mask: List of boolean numpy arrays indicating which points in
-        the light curve were used to fit the best-fit spline.
+      the light curve were used to fit the best-fit spline.
     bkspace: The break-point spacing used for the best-fit spline.
     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.
+      -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.
     bic: The value of the Bayesian Information Criterion; equal to
-        likelihood_term + penalty_coeff * penalty_term.
+      likelihood_term + penalty_coeff * penalty_term.
   """
 
   def __init__(self):
@@ -163,14 +163,13 @@ def choose_kepler_spline(all_time,
     all_flux: List of 1D numpy arrays; the flux values of the light curve.
     bkspaces: List of break-point spacings to try.
     maxiter: Maximum number of attempts to fit each spline after removing badly
-        fit points.
+      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
@@ -295,14 +294,13 @@ def fit_kepler_spline(all_time,
     bkspace_max: Maximum breakpoint spacing to try.
     bkspace_num: Number of breakpoint spacings to try.
     maxiter: Maximum number of attempts to fit each spline after removing badly
-        fit points.
+      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

research/astronet/third_party/robust_mean/robust_mean.py

@@ -17,7 +17,7 @@ def robust_mean(y, cut):
   Args:
     y: 1D numpy array. Assumed to be normally distributed with outliers.
     cut: Points more than this number of standard deviations from the median are
-        ignored.
+      ignored.
 
   Returns:
     mean: A robust estimate of the mean of y.