Merge pull request #4614 from cshallue/master

 Modularize light curve and TCE preprocessing functions for easier reuse

research/astronet/astronet/data/BUILD

@@ -8,10 +8,11 @@ py_binary(
     deps = [":preprocess"],
 )
 
-py_binary(
+py_library(
     name = "preprocess",
     srcs = ["preprocess.py"],
     deps = [
+        "//astronet/util:example_util",
         "//light_curve_util:kepler_io",
         "//light_curve_util:median_filter",
         "//light_curve_util:util",

research/astronet/astronet/data/generate_input_records.py

@@ -131,25 +131,6 @@ _LABEL_COLUMN = "av_training_set"
 _ALLOWED_LABELS = {"PC", "AFP", "NTP"}
 
 
-def _set_float_feature(ex, name, value):
-  """Sets the value of a float feature in a tensorflow.train.Example proto."""
-  assert name not in ex.features.feature, "Duplicate feature: %s" % name
-  ex.features.feature[name].float_list.value.extend([float(v) for v in value])
-
-
-def _set_bytes_feature(ex, name, value):
-  """Sets the value of a bytes feature in a tensorflow.train.Example proto."""
-  assert name not in ex.features.feature, "Duplicate feature: %s" % name
-  ex.features.feature[name].bytes_list.value.extend([
-      str(v).encode("latin-1") for v in value])
-
-
-def _set_int64_feature(ex, name, value):
-  """Sets the value of an int64 feature in a tensorflow.train.Example proto."""
-  assert name not in ex.features.feature, "Duplicate feature: %s" % name
-  ex.features.feature[name].int64_list.value.extend([int(v) for v in value])
-
-
 def _process_tce(tce):
   """Processes the light curve for a Kepler TCE and returns an Example proto.
 
@@ -158,39 +139,11 @@ def _process_tce(tce):
 
   Returns:
     A tensorflow.train.Example proto containing TCE features.
-
-  Raises:
-    IOError: If the light curve files for this Kepler ID cannot be found.
   """
-  # Read and process the light curve.
-  time, flux = preprocess.read_and_process_light_curve(tce.kepid,
-                                                       FLAGS.kepler_data_dir)
-  time, flux = preprocess.phase_fold_and_sort_light_curve(
-      time, flux, tce.tce_period, tce.tce_time0bk)
-
-  # Generate the local and global views.
-  global_view = preprocess.global_view(time, flux, tce.tce_period)
-  local_view = preprocess.local_view(time, flux, tce.tce_period,
-                                     tce.tce_duration)
-
-  # Make output proto.
-  ex = tf.train.Example()
-
-  # Set time series features.
-  _set_float_feature(ex, "global_view", global_view)
-  _set_float_feature(ex, "local_view", local_view)
-
-  # Set other columns.
-  for col_name, value in tce.items():
-    if np.issubdtype(type(value), np.integer):
-      _set_int64_feature(ex, col_name, [value])
-    else:
-      try:
-        _set_float_feature(ex, col_name, [float(value)])
-      except ValueError:
-        _set_bytes_feature(ex, col_name, [value])
-
-  return ex
+  all_time, all_flux = preprocess.read_light_curve(tce.kepid,
+                                                   FLAGS.kepler_data_dir)
+  time, flux = preprocess.process_light_curve(all_time, all_flux)
+  return preprocess.generate_example_for_tce(time, flux, tce)
 
 
 def _process_file_shard(tce_table, file_name):

research/astronet/astronet/data/preprocess.py

@@ -21,29 +21,28 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
+from astronet.util import example_util
 from light_curve_util import kepler_io
 from light_curve_util import median_filter
 from light_curve_util import util
 from third_party.kepler_spline import kepler_spline
 
 
-def read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75):
-  """Reads a light curve, fits a B-spline and divides the curve by the spline.
+def read_light_curve(kepid, kepler_data_dir):
+  """Reads a Kepler light curve.
 
   Args:
     kepid: Kepler id of the target star.
     kepler_data_dir: Base directory containing Kepler data. See
         kepler_io.kepler_filenames().
-    max_gap_width: Gap size (in days) above which the light curve is split for
-        the fitting of B-splines.
 
   Returns:
-    time: 1D NumPy array; the time values of the light curve.
-    flux: 1D NumPy array; the normalized flux values of the light curve.
+    all_time: A list of numpy arrays; the time values of the raw light curve.
+    all_flux: A list of numpy arrays corresponding to the time arrays in
+        all_time.
 
   Raises:
     IOError: If the light curve files for this Kepler ID cannot be found.
-    ValueError: If the spline could not be fit.
   """
   # Read the Kepler light curve.
   file_names = kepler_io.kepler_filenames(kepler_data_dir, kepid)
@@ -51,21 +50,26 @@ def read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75):
     raise IOError("Failed to find .fits files in %s for Kepler ID %s" %
                   (kepler_data_dir, kepid))
 
-  all_time, all_flux = kepler_io.read_kepler_light_curve(file_names)
+  return kepler_io.read_kepler_light_curve(file_names)
 
-  # Split on gaps.
-  all_time, all_flux = util.split(all_time, all_flux, gap_width=max_gap_width)
 
-  # Logarithmically sample candidate break point spacings between 0.5 and 20
-  # days.
-  bkspaces = np.logspace(np.log10(0.5), np.log10(20), num=20)
+def process_light_curve(all_time, all_flux):
+  """Removes low-frequency variability from a light curve.
 
-  # Generate spline.
-  spline = kepler_spline.choose_kepler_spline(
-      all_time, all_flux, bkspaces, penalty_coeff=1.0, verbose=False)[0]
+  Args:
+    all_time: A list of numpy arrays; the time values of the raw light curve.
+    all_flux: A list of numpy arrays corresponding to the time arrays in
+        all_time.
+
+  Returns:
+    time: 1D NumPy array; the time values of the light curve.
+    flux: 1D NumPy array; the normalized flux values of the light curve.
+  """
+  # Split on gaps.
+  all_time, all_flux = util.split(all_time, all_flux, gap_width=0.75)
 
-  if spline is None:
-    raise ValueError("Failed to fit spline with Kepler ID %s", kepid)
+  # Fit a piecewise-cubic spline with default arguments.
+  spline = kepler_spline.fit_kepler_spline(all_time, all_flux, verbose=False)[0]
 
   # Concatenate the piecewise light curve and spline.
   time = np.concatenate(all_time)
@@ -77,7 +81,6 @@ def read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75):
   # there. Instead we just remove them.
   finite_i = np.isfinite(spline)
   if not np.all(finite_i):
-    tf.logging.warn("Incomplete spline with Kepler ID %s", kepid)
     time = time[finite_i]
     flux = flux[finite_i]
     spline = spline[finite_i]
@@ -202,3 +205,38 @@ def local_view(time,
       bin_width=duration * bin_width_factor,
       t_min=max(-period / 2, -duration * num_durations),
       t_max=min(period / 2, duration * num_durations))
+
+
+def generate_example_for_tce(time, flux, tce):
+  """Generates a tf.train.Example representing an input TCE.
+
+  Args:
+    time: 1D NumPy array; the time values of the light curve.
+    flux: 1D NumPy array; the normalized flux values of the light curve.
+    tce: Dict-like object containing at least 'tce_period', 'tce_duration', and
+        'tce_time0bk'. Additional items are included as features in the output.
+
+  Returns:
+    A tf.train.Example containing features 'global_view', 'local_view', and all
+    values present in `tce`.
+  """
+  period = tce["tce_period"]
+  duration = tce["tce_duration"]
+  t0 = tce["tce_time0bk"]
+
+  time, flux = phase_fold_and_sort_light_curve(time, flux, period, t0)
+
+  # Make output proto.
+  ex = tf.train.Example()
+
+  # Set time series features.
+  example_util.set_float_feature(ex, "global_view",
+                                 global_view(time, flux, period))
+  example_util.set_float_feature(ex, "local_view",
+                                 local_view(time, flux, period, duration))
+
+  # Set other features in `tce`.
+  for name, value in tce.items():
+    example_util.set_feature(ex, name, [value])
+
+  return ex

research/astronet/astronet/util/BUILD

@@ -42,3 +42,17 @@ py_library(
         "//astronet/ops:training",
     ],
 )
+
+py_library(
+    name = "example_util",
+    srcs = ["example_util.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_test(
+    name = "example_util_test",
+    size = "small",
+    srcs = ["example_util_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":example_util"],
+)

research/astronet/astronet/util/example_util.py

+# Copyright 2018 The TensorFlow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Helpers for getting and setting values in tf.Example protocol buffers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+
+def get_feature(ex, name, kind=None, strict=True):
+  """Gets a feature value from a tf.train.Example.
+
+  Args:
+    ex: A tf.train.Example.
+    name: Name of the feature to look up.
+    kind: Optional: one of 'bytes_list', 'float_list', 'int64_list'. Inferred if
+        not specified.
+    strict: Whether to raise a KeyError if there is no such feature.
+
+  Returns:
+    A numpy array containing to the values of the specified feature.
+
+  Raises:
+    KeyError: If there is no feature with the specified name.
+    TypeError: If the feature has a different type to that specified.
+  """
+  if name not in ex.features.feature:
+    if strict:
+      raise KeyError(name)
+    return np.array([])
+
+  inferred_kind = ex.features.feature[name].WhichOneof("kind")
+  if not inferred_kind:
+    return np.array([])  # Feature exists, but it's empty.
+
+  if kind and kind != inferred_kind:
+    raise TypeError("Requested %s, but Feature has %s" % (kind, inferred_kind))
+
+  return np.array(getattr(ex.features.feature[name], inferred_kind).value)
+
+
+def get_bytes_feature(ex, name, strict=True):
+  """Gets the value of a bytes feature from a tf.train.Example."""
+  return get_feature(ex, name, "bytes_list", strict)
+
+
+def get_float_feature(ex, name, strict=True):
+  """Gets the value of a float feature from a tf.train.Example."""
+  return get_feature(ex, name, "float_list", strict)
+
+
+def get_int64_feature(ex, name, strict=True):
+  """Gets the value of an int64 feature from a tf.train.Example."""
+  return get_feature(ex, name, "int64_list", strict)
+
+
+def _infer_kind(value):
+  """Infers the tf.train.Feature kind from a value."""
+  if np.issubdtype(type(value[0]), np.integer):
+    return "int64_list"
+  try:
+    float(value[0])
+    return "float_list"
+  except ValueError:
+    return "bytes_list"
+
+
+def set_feature(ex, name, value, kind=None, allow_overwrite=False):
+  """Sets a feature value in a tf.train.Example.
+
+  Args:
+    ex: A tf.train.Example.
+    name: Name of the feature to set.
+    value: Feature value to set. Must be a sequence.
+    kind: Optional: one of 'bytes_list', 'float_list', 'int64_list'. Inferred if
+        not specified.
+    allow_overwrite: Whether to overwrite the existing value of the feature.
+
+  Raises:
+    ValueError: If `allow_overwrite` is False and the feature already exists, or
+        if `kind` is unrecognized.
+  """
+  if name in ex.features.feature:
+    if allow_overwrite:
+      del ex.features.feature[name]
+    else:
+      raise ValueError(
+          "Attempting to set duplicate feature with name: %s" % name)
+
+  if not kind:
+    kind = _infer_kind(value)
+
+  if kind == "bytes_list":
+    value = [str(v).encode("latin-1") for v in value]
+  elif kind == "float_list":
+    value = [float(v) for v in value]
+  elif kind == "int64_list":
+    value = [int(v) for v in value]
+  else:
+    raise ValueError("Unrecognized kind: %s" % kind)
+
+  getattr(ex.features.feature[name], kind).value.extend(value)
+
+
+def set_float_feature(ex, name, value, allow_overwrite=False):
+  """Sets the value of a float feature in a tf.train.Example."""
+  set_feature(ex, name, value, "float_list", allow_overwrite)
+
+
+def set_bytes_feature(ex, name, value, allow_overwrite=False):
+  """Sets the value of a bytes feature in a tf.train.Example."""
+  set_feature(ex, name, value, "bytes_list", allow_overwrite)
+
+
+def set_int64_feature(ex, name, value, allow_overwrite=False):
+  """Sets the value of an int64 feature in a tf.train.Example."""
+  set_feature(ex, name, value, "int64_list", allow_overwrite)

research/astronet/astronet/util/example_util_test.py

+# Copyright 2018 The TensorFlow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for example_util.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+from astronet.util import example_util
+
+
+class ExampleUtilTest(tf.test.TestCase):
+
+  def test_get_feature(self):
+    # Create Example.
+    bytes_list = tf.train.BytesList(
+        value=[v.encode("latin-1") for v in ["a", "b", "c"]])
+    float_list = tf.train.FloatList(value=[1.0, 2.0, 3.0])
+    int64_list = tf.train.Int64List(value=[11, 22, 33])
+    ex = tf.train.Example(
+        features=tf.train.Features(
+            feature={
+                "a_bytes": tf.train.Feature(bytes_list=bytes_list),
+                "b_float": tf.train.Feature(float_list=float_list),
+                "c_int64": tf.train.Feature(int64_list=int64_list),
+                "d_empty": tf.train.Feature(),
+            }))
+
+    # Get bytes feature.
+    np.testing.assert_array_equal(
+        example_util.get_feature(ex, "a_bytes").astype(str), ["a", "b", "c"])
+    np.testing.assert_array_equal(
+        example_util.get_feature(ex, "a_bytes", "bytes_list").astype(str),
+        ["a", "b", "c"])
+    np.testing.assert_array_equal(
+        example_util.get_bytes_feature(ex, "a_bytes").astype(str),
+        ["a", "b", "c"])
+    with self.assertRaises(TypeError):
+      example_util.get_feature(ex, "a_bytes", "float_list")
+    with self.assertRaises(TypeError):
+      example_util.get_float_feature(ex, "a_bytes")
+    with self.assertRaises(TypeError):
+      example_util.get_int64_feature(ex, "a_bytes")
+
+    # Get float feature.
+    np.testing.assert_array_almost_equal(
+        example_util.get_feature(ex, "b_float"), [1.0, 2.0, 3.0])
+    np.testing.assert_array_almost_equal(
+        example_util.get_feature(ex, "b_float", "float_list"), [1.0, 2.0, 3.0])
+    np.testing.assert_array_almost_equal(
+        example_util.get_float_feature(ex, "b_float"), [1.0, 2.0, 3.0])
+    with self.assertRaises(TypeError):
+      example_util.get_feature(ex, "b_float", "int64_list")
+    with self.assertRaises(TypeError):
+      example_util.get_bytes_feature(ex, "b_float")
+    with self.assertRaises(TypeError):
+      example_util.get_int64_feature(ex, "b_float")
+
+    # Get int64 feature.
+    np.testing.assert_array_equal(
+        example_util.get_feature(ex, "c_int64"), [11, 22, 33])
+    np.testing.assert_array_equal(
+        example_util.get_feature(ex, "c_int64", "int64_list"), [11, 22, 33])
+    np.testing.assert_array_equal(
+        example_util.get_int64_feature(ex, "c_int64"), [11, 22, 33])
+    with self.assertRaises(TypeError):
+      example_util.get_feature(ex, "c_int64", "bytes_list")
+    with self.assertRaises(TypeError):
+      example_util.get_bytes_feature(ex, "c_int64")
+    with self.assertRaises(TypeError):
+      example_util.get_float_feature(ex, "c_int64")
+
+    # Get empty feature.
+    np.testing.assert_array_equal(example_util.get_feature(ex, "d_empty"), [])
+    np.testing.assert_array_equal(
+        example_util.get_feature(ex, "d_empty", "float_list"), [])
+    np.testing.assert_array_equal(
+        example_util.get_bytes_feature(ex, "d_empty"), [])
+    np.testing.assert_array_equal(
+        example_util.get_float_feature(ex, "d_empty"), [])
+    np.testing.assert_array_equal(
+        example_util.get_int64_feature(ex, "d_empty"), [])
+
+    # Get nonexistent feature.
+    with self.assertRaises(KeyError):
+      example_util.get_feature(ex, "nonexistent")
+    with self.assertRaises(KeyError):
+      example_util.get_feature(ex, "nonexistent", "bytes_list")
+    with self.assertRaises(KeyError):
+      example_util.get_bytes_feature(ex, "nonexistent")
+    with self.assertRaises(KeyError):
+      example_util.get_float_feature(ex, "nonexistent")
+    with self.assertRaises(KeyError):
+      example_util.get_int64_feature(ex, "nonexistent")
+    np.testing.assert_array_equal(
+        example_util.get_feature(ex, "nonexistent", strict=False), [])
+    np.testing.assert_array_equal(
+        example_util.get_bytes_feature(ex, "nonexistent", strict=False), [])
+    np.testing.assert_array_equal(
+        example_util.get_float_feature(ex, "nonexistent", strict=False), [])
+    np.testing.assert_array_equal(
+        example_util.get_int64_feature(ex, "nonexistent", strict=False), [])
+
+  def test_set_feature(self):
+    ex = tf.train.Example()
+
+    # Set bytes features.
+    example_util.set_feature(ex, "a1_bytes", ["a", "b"])
+    example_util.set_feature(ex, "a2_bytes", ["A", "B"], kind="bytes_list")
+    example_util.set_bytes_feature(ex, "a3_bytes", ["x", "y"])
+    np.testing.assert_array_equal(
+        np.array(ex.features.feature["a1_bytes"].bytes_list.value).astype(str),
+        ["a", "b"])
+    np.testing.assert_array_equal(
+        np.array(ex.features.feature["a2_bytes"].bytes_list.value).astype(str),
+        ["A", "B"])
+    np.testing.assert_array_equal(
+        np.array(ex.features.feature["a3_bytes"].bytes_list.value).astype(str),
+        ["x", "y"])
+    with self.assertRaises(ValueError):
+      example_util.set_feature(ex, "a3_bytes", ["xxx"])  # Duplicate.
+
+    # Set float features.
+    example_util.set_feature(ex, "b1_float", [1.0, 2.0])
+    example_util.set_feature(ex, "b2_float", [10.0, 20.0], kind="float_list")
+    example_util.set_float_feature(ex, "b3_float", [88.0, 99.0])
+    np.testing.assert_array_almost_equal(
+        ex.features.feature["b1_float"].float_list.value, [1.0, 2.0])
+    np.testing.assert_array_almost_equal(
+        ex.features.feature["b2_float"].float_list.value, [10.0, 20.0])
+    np.testing.assert_array_almost_equal(
+        ex.features.feature["b3_float"].float_list.value, [88.0, 99.0])
+    with self.assertRaises(ValueError):
+      example_util.set_feature(ex, "b3_float", [1234.0])  # Duplicate.
+
+    # Set int64 features.
+    example_util.set_feature(ex, "c1_int64", [1, 2, 3])
+    example_util.set_feature(ex, "c2_int64", [11, 22, 33], kind="int64_list")
+    example_util.set_int64_feature(ex, "c3_int64", [88, 99])
+    np.testing.assert_array_equal(
+        ex.features.feature["c1_int64"].int64_list.value, [1, 2, 3])
+    np.testing.assert_array_equal(
+        ex.features.feature["c2_int64"].int64_list.value, [11, 22, 33])
+    np.testing.assert_array_equal(
+        ex.features.feature["c3_int64"].int64_list.value, [88, 99])
+    with self.assertRaises(ValueError):
+      example_util.set_feature(ex, "c3_int64", [1234])  # Duplicate.
+
+    # Overwrite features.
+    example_util.set_feature(ex, "a3_bytes", ["xxx"], allow_overwrite=True)
+    np.testing.assert_array_equal(
+        np.array(ex.features.feature["a3_bytes"].bytes_list.value).astype(str),
+        ["xxx"])
+
+    example_util.set_feature(ex, "b3_float", [1234.0], allow_overwrite=True)
+    np.testing.assert_array_almost_equal(
+        ex.features.feature["b3_float"].float_list.value, [1234.0])
+
+    example_util.set_feature(ex, "c3_int64", [1234], allow_overwrite=True)
+    np.testing.assert_array_equal(
+        ex.features.feature["c3_int64"].int64_list.value, [1234])
+
+
+if __name__ == "__main__":
+  tf.test.main()

research/astronet/light_curve_util/kepler_io.py

@@ -23,6 +23,7 @@ import os.path
 from astropy.io import fits
 import numpy as np
 
+from tensorflow import gfile
 
 LONG_CADENCE_TIME_DELTA_DAYS = 0.02043422  # Approximately 29.4 minutes.
 
@@ -135,7 +136,7 @@ def kepler_filenames(base_dir,
                                            cadence_suffix)
       filename = os.path.join(base_dir, base_name)
       # Not all stars have data for all quarters.
-      if not check_existence or os.path.isfile(filename):
+      if not check_existence or gfile.Exists(filename):
         filenames.append(filename)
 
   return filenames
@@ -160,7 +161,7 @@ def read_kepler_light_curve(filenames,
   all_flux = []
 
   for filename in filenames:
-    with fits.open(open(filename, "rb")) as hdu_list:
+    with fits.open(gfile.Open(filename, "rb")) as hdu_list:
       light_curve = hdu_list[light_curve_extension].data
       time = light_curve.TIME
       flux = light_curve.PDCSAP_FLUX

research/astronet/light_curve_util/util.py

@@ -78,7 +78,11 @@ def split(all_time, all_flux, gap_width=0.75):
   return out_time, out_flux
 
 
-def remove_events(all_time, all_flux, events, width_factor=1.0):
+def remove_events(all_time,
+                  all_flux,
+                  events,
+                  width_factor=1.0,
+                  include_empty_segments=True):
   """Removes events from a light curve.
 
   This function accepts either a single-segment or piecewise-defined light
@@ -91,6 +95,7 @@ def remove_events(all_time, all_flux, events, width_factor=1.0):
         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.
 
   Returns:
     output_time: Numpy array or list of numpy arrays; the time arrays with
@@ -118,7 +123,7 @@ def remove_events(all_time, all_flux, events, width_factor=1.0):
     if single_segment:
       output_time = time[mask]
       output_flux = flux[mask]
-    else:
+    elif include_empty_segments or np.any(mask):
       output_time.append(time[mask])
       output_flux.append(flux[mask])
 

research/astronet/light_curve_util/util_test.py

@@ -152,6 +152,30 @@ class LightCurveUtilTest(absltest.TestCase):
     self.assertSequenceAlmostEqual([10, 17, 18], output_time[1])
     self.assertSequenceAlmostEqual([100, 170, 180], output_flux[1])
 
+    # One segment totally removed with include_empty_segments = True.
+    time = [np.arange(5, dtype=np.float), np.arange(10, 20, dtype=np.float)]
+    flux = [10 * t for t in time]
+    events = [periodic_event.Event(period=10, duration=2, t0=2.5)]
+    output_time, output_flux = util.remove_events(
+        time, flux, events, width_factor=3, include_empty_segments=True)
+    self.assertLen(output_time, 2)
+    self.assertLen(output_flux, 2)
+    self.assertSequenceEqual([], output_time[0])
+    self.assertSequenceEqual([], output_flux[0])
+    self.assertSequenceAlmostEqual([16, 17, 18, 19], output_time[1])
+    self.assertSequenceAlmostEqual([160, 170, 180, 190], output_flux[1])
+
+    # One segment totally removed with include_empty_segments = False.
+    time = [np.arange(5, dtype=np.float), np.arange(10, 20, dtype=np.float)]
+    flux = [10 * t for t in time]
+    events = [periodic_event.Event(period=10, duration=2, t0=2.5)]
+    output_time, output_flux = util.remove_events(
+        time, flux, events, width_factor=3, include_empty_segments=False)
+    self.assertLen(output_time, 1)
+    self.assertLen(output_flux, 1)
+    self.assertSequenceAlmostEqual([16, 17, 18, 19], output_time[0])
+    self.assertSequenceAlmostEqual([160, 170, 180, 190], output_flux[0])
+
   def testInterpolateMaskedSpline(self):
     all_time = [
         np.arange(0, 10, dtype=np.float),

research/astronet/third_party/kepler_spline/kepler_spline.py

@@ -159,8 +159,7 @@ def choose_kepler_spline(all_time,
 
   Args:
     all_time: List of 1D numpy arrays; the time values of the light curve.
-    all_flux: List of 1D numpy arrays; the flux (brightness) values of the light
-        curve.
+    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.
@@ -187,7 +186,8 @@ def choose_kepler_spline(all_time,
   # model and sigma is the constant standard deviation for all flux values.
   # Moreover, we assume that s[i] ~= s[i+1]. Therefore,
   # (f[i+1] - f[i]) / sqrt(2) ~ N(0, sigma^2).
-  scaled_diffs = np.concatenate([np.diff(f) / np.sqrt(2) for f in all_flux])
+  scaled_diffs = [np.diff(f) / np.sqrt(2) for f in all_flux]
+  scaled_diffs = np.concatenate(scaled_diffs) if scaled_diffs else np.array([])
   if not scaled_diffs.size:
     best_spline = [np.array([np.nan] * len(f)) for f in all_flux]
     metadata.light_curve_mask = [
@@ -275,3 +275,48 @@ def choose_kepler_spline(all_time,
     ]
 
   return best_spline, metadata
+
+
+def fit_kepler_spline(all_time,
+                      all_flux,
+                      bkspace_min=0.5,
+                      bkspace_max=20,
+                      bkspace_num=20,
+                      maxiter=5,
+                      penalty_coeff=1.0,
+                      verbose=True):
+  """Fits a Kepler spline with logarithmically-sampled breakpoint spacings.
+
+  Args:
+    all_time: List of 1D numpy arrays; the time values of the light curve.
+    all_flux: List of 1D numpy arrays; the flux values of the light curve.
+    bkspace_min: Minimum breakpoint spacing to try.
+    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.
+    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.
+    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.
+
+  Returns:
+    spline: List of numpy arrays; values of the best-fit spline corresponding to
+        to the input flux arrays.
+    metadata: Object containing metadata about the spline fit.
+  """
+  # Logarithmically sample bkspace_num candidate break point spacings between
+  # bkspace_min and bkspace_max.
+  bkspaces = np.logspace(
+      np.log10(bkspace_min), np.log10(bkspace_max), num=bkspace_num)
+
+  return choose_kepler_spline(
+      all_time,
+      all_flux,
+      bkspaces,
+      maxiter=maxiter,
+      penalty_coeff=penalty_coeff,
+      verbose=verbose)

research/astronet/third_party/kepler_spline/kepler_spline_test.py

@@ -79,6 +79,19 @@ class KeplerSplineTest(absltest.TestCase):
 
 class ChooseKeplerSplineTest(absltest.TestCase):
 
+  def testEmptyInput(self):
+    # Logarithmically sample candidate break point spacings.
+    bkspaces = np.logspace(np.log10(0.5), np.log10(5), num=20)
+
+    spline, metadata = kepler_spline.choose_kepler_spline(
+        all_time=[],
+        all_flux=[],
+        bkspaces=bkspaces,
+        penalty_coeff=1.0,
+        verbose=False)
+    np.testing.assert_array_equal(spline, [])
+    np.testing.assert_array_equal(metadata.light_curve_mask, [])
+
   def testNoPoints(self):
     all_time = [np.array([])]
     all_flux = [np.array([])]