def _process_tce(feature_config):
"""Reads and process the input features of a Threshold Crossing Event.
Args:
feature_config: ConfigDict containing the feature configurations.
Returns:
A dictionary of processed light curve features.
Raises:
ValueError: If feature_config contains features other than 'global_view'
and 'local_view'.
"""
if not {"global_view", "local_view"}.issuperset(feature_config.keys()):
raise ValueError(
"Only 'global_view' and 'local_view' features are supported.")
# Read and process the light curve.
all_time, all_flux = preprocess.read_light_curve(FLAGS.kepler_id,
FLAGS.kepler_data_dir)
time, flux = preprocess.process_light_curve(all_time, all_flux)
time, flux = preprocess.phase_fold_and_sort_light_curve(
time, flux, FLAGS.period, FLAGS.t0)
# Generate the local and global views.
features = {}
if "global_view" in feature_config:
global_view = preprocess.global_view(time, flux, FLAGS.period)
# Add a batch dimension.
features["global_view"] = np.expand_dims(global_view, 0)
if "local_view" in feature_config:
local_view = preprocess.local_view(time, flux, FLAGS.period, FLAGS.duration)
# Add a batch dimension.
features["local_view"] = np.expand_dims(local_view, 0)
# Possibly save plots.
if FLAGS.output_image_file:
ncols = len(features)
fig, axes = plt.subplots(1, ncols, figsize=(10 * ncols, 5), squeeze=False)
for i, name in enumerate(sorted(features)):
ax = axes[0][i]
ax.plot(features[name][0], ".")
ax.set_title(name)
ax.set_xlabel("Bucketized Time (days)")
ax.set_ylabel("Normalized Flux")
fig.tight_layout()
fig.savefig(FLAGS.output_image_file, bbox_inches="tight")
return features
def _process_tce(tce):
"""Processes the light curve for a Kepler TCE and returns an Example proto.
Args:
tce: Row of the input TCE table.
Returns:
A tensorflow.train.Example proto containing TCE features.
"""
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_tce(tce):
"""Processes the light curve for a Kepler TCE and returns an Example proto.
Args:
tce: Row of the input TCE table.
Returns:
A tensorflow.train.Example proto containing TCE features.
"""
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)