def compute_and_save_periods(rrlyrae, Model, outfile,
model_args=None, model_kwds=None,
Nperiods=5, save_every=5,
parallel=True, client=None,
num_results=None):
"""Function to compute periods and save the results"""
cache = NumpyCache(outfile)
lcids = rrlyrae.ids
if num_results is not None:
lcids = lcids[:num_results]
# Define a function which, given an lcid, computes the desired periods.
def find_periods(lcid, Nperiods=Nperiods,
rrlyrae=rrlyrae, Model=Model,
model_args=model_args, model_kwds=model_kwds):
t, y, dy, filts = rrlyrae.get_lightcurve(lcid)
model = Model(*(model_args or ()), **(model_kwds or {}))
model.optimizer.period_range = (0.2, 1.2)
model.optimizer.verbose = 0
model.fit(t, y, dy, filts)
return lcid, model.find_best_periods(Nperiods)
return compute_parallel(cache, find_periods, lcids,
save_every=save_every,
parallel=parallel, client=client)
def compute_and_save_periods(Model,
outfile,
pointing_indices,
ndays,
gmags,
template_indices,
model_args=None,
model_kwds=None,
Nperiods=5,
save_every=5,
parallel=True,
client=None,
num_results=None):
"""Function to compute periods and save the results"""
cache = NumpyCache(outfile)
keys = list(np.broadcast(pointing_indices, ndays, gmags, template_indices))
if num_results is not None:
keys = keys[:num_results]
# Define a function which, given a key, computes the desired periods.
def find_periods(key,
Nperiods=Nperiods,
Model=Model,
LSSTsims=LSSTsims,
model_args=model_args,
model_kwds=model_kwds):
import numpy as np
lsstsim = LSSTsims()
t, y, dy, filts = lsstsim.generate_lc(*key, random_state=0)
model = Model(*(model_args or ()), **(model_kwds or {}))
model.optimizer.period_range = (0.2, 1.2)
model.optimizer.verbose = 0
model.fit(t, y, dy, filts)
try:
periods = model.find_best_periods(Nperiods)
except np.linalg.LinAlgError:
periods = np.nan + np.zeros(Nperiods)
except ValueError:
periods = np.nan + np.zeros(Nperiods)
return key, periods
results = compute_parallel(cache,
find_periods,
keys,
save_every=save_every,
parallel=parallel,
client=client)
if num_results is not None:
return results
else:
return gather_results(outfile, pointing_indices, ndays, gmags,
template_indices)
def compute_and_save_periods(
Model,
outfile,
pointing_indices,
ndays,
gmags,
template_indices,
model_args=None,
model_kwds=None,
Nperiods=5,
save_every=5,
parallel=True,
client=None,
num_results=None,
):
"""Function to compute periods and save the results"""
cache = NumpyCache(outfile)
keys = list(np.broadcast(pointing_indices, ndays, gmags, template_indices))
if num_results is not None:
keys = keys[:num_results]
# Define a function which, given a key, computes the desired periods.
def find_periods(
key, Nperiods=Nperiods, Model=Model, LSSTsims=LSSTsims, model_args=model_args, model_kwds=model_kwds
):
import numpy as np
lsstsim = LSSTsims()
t, y, dy, filts = lsstsim.generate_lc(*key, random_state=0)
model = Model(*(model_args or ()), **(model_kwds or {}))
model.optimizer.period_range = (0.2, 1.2)
model.optimizer.verbose = 0
model.fit(t, y, dy, filts)
try:
periods = model.find_best_periods(Nperiods)
except np.linalg.LinAlgError:
periods = np.nan + np.zeros(Nperiods)
except ValueError:
periods = np.nan + np.zeros(Nperiods)
return key, periods
results = compute_parallel(cache, find_periods, keys, save_every=save_every, parallel=parallel, client=client)
if num_results is not None:
return results
else:
return gather_results(outfile, pointing_indices, ndays, gmags, template_indices)
def compute_and_save_periods(rrlyrae,
Model,
outfile,
model_args=None,
model_kwds=None,
Nperiods=5,
save_every=5,
parallel=True,
client=None,
num_results=None):
"""Function to compute periods and save the results"""
cache = NumpyCache(outfile)
lcids = rrlyrae.ids
if num_results is not None:
lcids = lcids[:num_results]
# Define a function which, given an lcid, computes the desired periods.
def find_periods(lcid,
Nperiods=Nperiods,
rrlyrae=rrlyrae,
Model=Model,
model_args=model_args,
model_kwds=model_kwds):
t, y, dy, filts = rrlyrae.get_lightcurve(lcid)
model = Model(*(model_args or ()), **(model_kwds or {}))
model.optimizer.period_range = (0.2, 1.2)
model.optimizer.verbose = 0
model.fit(t, y, dy, filts)
return lcid, model.find_best_periods(Nperiods)
return compute_parallel(cache,
find_periods,
lcids,
save_every=save_every,
parallel=parallel,
client=client)
