elif SURR_TYPE == 'AR':
sg.add_seasonality(mean[:-1, ...], 1, 0)
amp_surrs = np.zeros_like(sg.surr_data)
job_args = [(i, j, s0_amp, sg.surr_data[:, i, j])
for i in range(sg.lats.shape[0])
for j in range(sg.lons.shape[0])]
log(map_func)
job_results = map_func(_get_amplitude, job_args)
del job_args
# map results
for i, j, amp in job_results:
amp_surrs[:, i, j] = amp
del job_results
sg.surr_data = sg.surr_data[IDX, ...]
phase_surrs = phase_surrs[IDX, ...]
if AMPLITUDE:
amp_surrs = amp_surrs[IDX, ...]
if SEASON != None:
sg.surr_data = sg.surr_data[NDX_SEASON, ...]
phase_surrs = phase_surrs[NDX_SEASON, ...]
if AMPLITUDE:
job_args = [(i, j, phase_surrs[:, i, j],
amp_surrs[:, i, j], phase_bins)
for i in range(sg.lats.shape[0])
for j in range(sg.lons.shape[0])]
else:
job_args = [(i, j, phase_surrs[:, i, j],
if SURR_TYPE == 'MF' or SURR_TYPE == 'FT':
sg.add_seasonality(mean, 1, 0)
elif SURR_TYPE == 'AR':
sg.add_seasonality(mean[:-1, ...], 1, 0)
amp_surrs = np.zeros_like(sg.surr_data)
job_args = [ (i, j, s0_amp, sg.surr_data[:, i, j]) for i in range(sg.lats.shape[0]) for j in range(sg.lons.shape[0]) ]
log(map_func)
job_results = map_func(_get_amplitude, job_args)
del job_args
# map results
for i, j, amp in job_results:
amp_surrs[:, i, j] = amp
del job_results
sg.surr_data = sg.surr_data[IDX, ...]
phase_surrs = phase_surrs[IDX, ...]
if AMPLITUDE:
amp_surrs = amp_surrs[IDX, ...]
if SEASON != None:
sg.surr_data = sg.surr_data[NDX_SEASON, ...]
phase_surrs = phase_surrs[NDX_SEASON, ...]
if AMPLITUDE:
job_args = [ (i, j, phase_surrs[:, i, j], amp_surrs[:, i, j], phase_bins) for i in range(sg.lats.shape[0]) for j in range(sg.lons.shape[0]) ]
else:
job_args = [ (i, j, phase_surrs[:, i, j], sg.surr_data[:, i, j], phase_bins) for i in range(sg.lats.shape[0]) for j in range(sg.lons.shape[0]) ]
job_result = map_func(_get_cond_means, job_args)
del job_args, phase_surrs
# map results
