def bspline_clean_dataset(dataset, genome, loci, prediction_steps):
"""Clean a dataset containing temperatures and loads using cleaning
parameters from the genome. The dataset is expected to contain NaNs in the
last *prediction_steps* elements of the Load series"""
# Having the smoother as a global is not nice, but it speeds up things A
# LOT, because pickling the smoother caches takes a long time for large
# matrices (long time series).
global _smoother, _temp_cache, _load_cache
if _smoother is None:
_smoother = cln.BSplineSmoother(dataset, smoothness=1)
clean_data = dataset.copy()
key = (_get_dataset_hash(dataset["Temperature"]),
genome[loci.t_smooth], genome[loci.t_zscore])
try:
_temp_mutex.acquire()
clean_data['Temperature'] = _temp_cache[key].copy()
# print "Got temp from cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
except KeyError:
_temp_mutex.release()
# print "Storing temp to cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
clean_data['Temperature'] = \
cln.bspline_clean(dataset['Temperature'],
genome[loci.t_smooth],
genome[loci.t_zscore], _smoother)
_temp_mutex.acquire()
_temp_cache[key] = clean_data['Temperature'].copy()
_temp_mutex.release()
key = (_get_dataset_hash(dataset["Load"]),
genome[loci.l_smooth], genome[loci.l_zscore])
try:
_load_mutex.acquire()
clean_data['Load'][:-prediction_steps] = _load_cache[key].copy()
# print "Got load from cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
except KeyError:
_load_mutex.release()
# print "Storing load to cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
clean_data['Load'][:-prediction_steps] = \
cln.bspline_clean(dataset['Load'][:-prediction_steps],
genome[loci.l_smooth],
genome[loci.l_zscore], _smoother)
_load_mutex.acquire()
_load_cache[key] = clean_data['Load'][:-prediction_steps].copy()
_load_mutex.release()
return clean_data
def bspline_clean_dataset(dataset, genome, loci, prediction_steps):
"""Clean a dataset containing temperatures and loads using cleaning
parameters from the genome. The dataset is expected to contain NaNs in the
last *prediction_steps* elements of the Load series"""
# Having the smoother as a global is not nice, but it speeds up things A
# LOT, because pickling the smoother caches takes a long time for large
# matrices (long time series).
global _smoother, _temp_cache, _load_cache
if _smoother is None:
_smoother = cln.BSplineSmoother(dataset, smoothness=1)
clean_data = dataset.copy()
key = (_get_dataset_hash(dataset["Temperature"]), genome[loci.t_smooth],
genome[loci.t_zscore])
try:
_temp_mutex.acquire()
clean_data['Temperature'] = _temp_cache[key].copy()
# print "Got temp from cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
except KeyError:
_temp_mutex.release()
# print "Storing temp to cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
clean_data['Temperature'] = \
cln.bspline_clean(dataset['Temperature'],
genome[loci.t_smooth],
genome[loci.t_zscore], _smoother)
_temp_mutex.acquire()
_temp_cache[key] = clean_data['Temperature'].copy()
_temp_mutex.release()
key = (_get_dataset_hash(dataset["Load"]), genome[loci.l_smooth],
genome[loci.l_zscore])
try:
_load_mutex.acquire()
clean_data['Load'][:-prediction_steps] = _load_cache[key].copy()
# print "Got load from cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
except KeyError:
_load_mutex.release()
# print "Storing load to cache: <dataset_hash>", key[1], key[2]
# sys.stdout.flush()
clean_data['Load'][:-prediction_steps] = \
cln.bspline_clean(dataset['Load'][:-prediction_steps],
genome[loci.l_smooth],
genome[loci.l_zscore], _smoother)
_load_mutex.acquire()
_load_cache[key] = clean_data['Load'][:-prediction_steps].copy()
_load_mutex.release()
return clean_data
def bspline_clean_dataset_no_cache(dataset, genome, loci, prediction_steps):
"""Clean a dataset containing temperatures and loads using cleaning
parameters from the genome. The dataset is expected to contain NaNs in the
last *prediction_steps* elements of the Load series"""
# Having the smoother as a global is not nice, but it speeds up things A
# LOT, because pickling the smoother caches takes a long time for large
# matrices (long time series).
global _smoother
if _smoother is None:
_smoother = cln.BSplineSmoother(dataset, smoothness=1)
clean_data = dataset.copy()
clean_data['Temperature'] = cln.bspline_clean(dataset['Temperature'],
genome[loci.t_smooth],
genome[loci.t_zscore], _smoother)
clean_data['Load'][:-prediction_steps] = \
cln.bspline_clean(dataset['Load'][:-prediction_steps],
genome[loci.l_smooth],
genome[loci.l_zscore], _smoother)
return clean_data
def bspline_clean_dataset_no_cache(dataset, genome, loci, prediction_steps):
"""Clean a dataset containing temperatures and loads using cleaning
parameters from the genome. The dataset is expected to contain NaNs in the
last *prediction_steps* elements of the Load series"""
# Having the smoother as a global is not nice, but it speeds up things A
# LOT, because pickling the smoother caches takes a long time for large
# matrices (long time series).
global _smoother
if _smoother is None:
_smoother = cln.BSplineSmoother(dataset, smoothness=1)
clean_data = dataset.copy()
clean_data['Temperature'] = cln.bspline_clean(dataset['Temperature'],
genome[loci.t_smooth],
genome[loci.t_zscore],
_smoother)
clean_data['Load'][:-prediction_steps] = \
cln.bspline_clean(dataset['Load'][:-prediction_steps],
genome[loci.l_smooth],
genome[loci.l_zscore], _smoother)
return clean_data
