def process_light_curves(self):
processed_lightcurves = read_multiple_light_curves(
self.light_curves,
known_redshift=self.known_redshift,
training_set_parameters=None)
prepareinputarrays = PrepareInputArrays(self.passbands,
self.contextual_info)
X = prepareinputarrays.prepare_input_arrays(processed_lightcurves)
return X
def _get_custom_data(self, class_num, data_dir, save_dir, passbands,
known_redshift, nprocesses, redo):
"""Function for traning purposes.
Notes
-----
See astrorapid for API usage.
"""
light_list, target_list = p2r.convert(self._curves, self._metadata)
# now we need to preprocess
return read_multiple_light_curves(light_list)
def process_light_curves(self):
processed_lightcurves = read_multiple_light_curves(
self.light_curves,
known_redshift=self.known_redshift,
training_set_parameters=None)
prepareinputarrays = PrepareInputArrays(self.passbands,
self.contextual_info,
self.bcut, self.zcut)
X, orig_lc, timesX, objids_list, trigger_mjds = prepareinputarrays.prepare_input_arrays(
processed_lightcurves)
return X, orig_lc, timesX, objids_list, trigger_mjds
def process_light_curves(self, light_curves):
processed_lightcurves = read_multiple_light_curves(light_curves, known_redshift=self.known_redshift,
training_set_parameters=None)
prepareinputarrays = PrepareInputArrays(self.passbands, self.contextual_info, self.bcut, self.zcut,
self.nobs, self.mintime, self.maxtime, self.timestep)
X, orig_lc, timesX, objids_list, trigger_mjds = prepareinputarrays.prepare_input_arrays(processed_lightcurves)
# # REMOVE CORRECTION FACTOR IF NOT USED
# correction_factor = np.load('astrorapid/correction_factor.npy')
# for i, pb in enumerate(self.passbands):
# X[:, :, i] = X[:, :, i] / correction_factor[i]
return X, orig_lc, timesX, objids_list, trigger_mjds
def _get_data(class_num, data_dir, save_dir, passbands, known_redshift,
nprocesses, redo, calculate_t0):
# This is a function RAPID needs to call to get the data.
# get the class num tuple
class_map = {
key: value
for (key, value) in p2r.class_map.items() if value is class_num
}
band_map = {
key: value
for (key, value) in p2r.band_map.items() if key in passbands
}
light_list, target_list = p2r.convert(curves,
metadata,
classes=class_map,
bands=band_map)
# now we need to preprocess
return read_multiple_light_curves(light_list)
def process_light_curves(self, light_curves, other_meta_data=None):
"""
Parameters
----------
light_curves: list of tuples
Each tuple in the list is of the form: (mjd, flux, fluxerr, passband, photflag, ra, dec, objid, redshift, mwebv)
for each transient.
other_meta_data: list of dictionaries or None
Each dictionary in the list contains any additional meta data to be used as contextual_info
when classifying (if the classifier being used was trained on the specified contextual_info).
E.g. other_meta_data = [{'hosttype': 3, 'host_dist': 200}, {'hosttype': 2, 'host_dist': 150},]
Returns
-------
"""
processed_lightcurves = read_multiple_light_curves(
light_curves,
known_redshift=self.known_redshift,
training_set_parameters=None,
other_meta_data=other_meta_data)
prepareinputarrays = PrepareInputArrays(self.passbands,
self.contextual_info,
self.bcut, self.zcut,
self.nobs, self.mintime,
self.maxtime, self.timestep)
X, orig_lc, timesX, objids_list, trigger_mjds = prepareinputarrays.prepare_input_arrays(
processed_lightcurves)
# # REMOVE CORRECTION FACTOR IF NOT USED
# correction_factor = np.load('astrorapid/correction_factor.npy')
# for i, pb in enumerate(self.passbands):
# X[:, :, i] = X[:, :, i] / correction_factor[i]
return X, orig_lc, timesX, objids_list, trigger_mjds
def get_light_curves(self, light_curves_list):
""" Returns dictionary of light curve information with each object ID as a key. """
processed_lightcurves = read_multiple_light_curves(light_curves_list, known_redshift=False,
training_set_parameters=None)
return processed_lightcurves