def _process_tce(feature_config, tce):
"""Reads and process the input features of a Threshold Crossing Event.
Args:
tce: row from TCE CSV file, read in as a Pandas dataframe.
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.
time, flux = preprocess.read_and_process_light_curve(tce.tic_id,
FLAGS.tess_data_dir,
sector=tce.Sectors)
time, flux = preprocess.phase_fold_and_sort_light_curve(
time, flux, tce.Period, tce.Epoc)
# Generate the local and global views.
features = {}
if "global_view" in feature_config:
global_view = preprocess.global_view(time, flux, tce.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, tce.Period,
tce.Duration)
# Add a batch dimension.
features["local_view"] = np.expand_dims(local_view, 0)
# Possibly save plots.
if FLAGS.output_image_dir:
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(os.path.join(FLAGS.output_image_dir,
str(tce.tic_id) + '.png'),
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.
Raises:
IOError: If the light curve files for this Kepler ID cannot be found.
"""
# Read and process the light curve.
CAMPAIGN = "C" + str(tce.campaign)
# Make output proto.
ex = tf.train.Example()
try:
time, flux = preprocess.read_and_process_light_curve(
tce.kepid, FLAGS.kepler_data_dir, CAMPAIGN)
except ValueError:
print("NO SIGMA????? ep" + str(tce.kepid))
return None
time, flux = preprocess.phase_fold_and_sort_light_curve(
time, flux, tce.tce_period, tce.tce_time0bk)
# Generate the local and global views.
try:
global_view = preprocess.global_view(time, flux, tce.tce_period)
local_view = preprocess.local_view(time, flux, tce.tce_period,
tce.tce_duration)
except ValueError:
print("BROKEN LIGHTCURVE????? ep" + str(tce.kepid))
return None
except Exception as e:
print(e.args)
print("Bins Broken ep" + str(tce.kepid))
return None
# 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
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.
time, flux = preprocess.read_and_process_light_curve(FLAGS.kepler_id,
FLAGS.kepler_data_dir)
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.
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.tic_id,
FLAGS.tess_data_dir,
sector=tce.Sectors,
is_multi=tce.is_multi)
time, flux = preprocess.phase_fold_and_sort_light_curve(
time, flux, tce.Period, tce.Epoc)
# Generate the local and global views.
try:
global_view = preprocess.global_view(time, flux, tce.Period)
local_view = preprocess.local_view(time, flux, tce.Period,
tce.Duration)
# secondary_view = preprocess.secondary_view(time, flux, tce.Period, tce.Duration)
except RuntimeWarning:
tf.compat.v1.logging.info('Too many invalid values in TIC %s',
tce.tic_id)
raise SparseLightCurveError
# 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_float_feature(ex, "secondary_view", secondary_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
def _process_tce(feature_config, kepler_id, period, t0, duration, kepler_data):
time, flux = preprocess.read_and_process_light_curve(
kepler_id, kepler_data)
time, flux = preprocess.phase_fold_and_sort_light_curve(
time, flux, period, t0)
features = {}
if "global_view" in feature_config:
global_view = preprocess.global_view(time, flux, period)
features["global_view"] = np.expand_dims(global_view, 0)
if "local_view" in feature_config:
local_view = preprocess.local_view(time, flux, period, duration)
features["local_view"] = np.expand_dims(local_view, 0)
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.
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.iteritems():
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, [str(value)])
return ex
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.")
kep_id = FLAGS.kepler_id
filenames = kepler_io.kepler_filenames('data/kepler', kep_id)
all_time, all_flux = kepler_io.read_kepler_light_curve(filenames)
for f in all_flux:
f /= np.median(f)
plt.figure(figsize=(12, 6), dpi=80)
plt.scatter(np.concatenate(all_time),
np.concatenate(all_flux),
s=0.5,
cmap='plasma')
plt.title('Entire Kepler Mission Data for KEP_ID=' + str(kep_id))
plt.xlabel('Time(days)')
plt.ylabel('Brightness')
plt.show()
# Read and process the light curve.
time, flux, spline = preprocess.read_and_process_light_curve(
FLAGS.kepler_id, FLAGS.kepler_data_dir)
#plt.figure(figsize=(12, 6), dpi=80)
#plt.scatter(np.concatenate(all_time), np.concatenate(all_flux), s=0.5, cmap='plasma')
plt.plot(spline, 'r')
plt.title('Best fit spline')
#plt.xlabel('Time(days)')
#plt.ylabel('Brightness')
#plt.show()
plt.figure(figsize=(12, 6), dpi=80)
plt.scatter(time, flux, s=0.5, cmap='plasma')
plt.title('Divided by best fit spline')
plt.xlabel('Time(days)')
plt.ylabel('Brightness')
plt.show()
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)
plt.figure(figsize=(12, 6), dpi=80)
plt.plot(range(201), local_view, 'o')
plt.title('Folded local view of target TCE')
plt.xlabel('Time(days)')
plt.ylabel('Brightness')
plt.show()
# 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
