for i in range(NUM_SURR):
jobQ.put(1)
for i in range(WORKERS):
jobQ.put(None)
a = g_surrs.get_seasonality(DETREND=True)
sg = SurrogateField()
sg.copy_field(g_surrs)
if AMPLITUDE:
a_amp = g_surrs_amp.get_seasonality(True)
sg_amp = SurrogateField()
sg_amp.copy_field(g_surrs_amp)
else:
sg_amp = None
a_amp = None
if SURR_TYPE == 'AR':
sg.prepare_AR_surrogates()
if AMPLITUDE:
sg_amp.prepare_AR_surrogates()
workers = [
Process(target=_cond_difference_surrogates,
args=(sg, sg_amp, g_surrs, a, a_amp, start_cut, jobQ,
resQ, ndx_season)) for iota in range(WORKERS)
]
for w in workers:
w.start()
tot = 0
while surr_completed < NUM_SURR:
# get result
diff, meanVar, cond_means_surr, maxmin = resQ.get()
if CONDITION:
if (maxmin > 2) and (maxmin < 6):
else:
job_args = [(i, j, phase_data[:, i, j], g.data[:, i, j], phase_bins)
for i in range(g.lats.shape[0])
for j in range(g.lons.shape[0])]
job_result = map_func(_get_cond_means, job_args)
del job_args, phase_data
# map results
for i, j, cmm, cmv in job_result:
bins_data[i, j, :] = cmm
bins_data_var[i, j, :] = cmv
del job_result
# if surrogate type is AR, exploit the pool to prepare the AR model
if SURR_TYPE == 'AR' or SURR_TYPE == 'ALL':
log("Creating AR(1) model and computing residuals...")
sg.prepare_AR_surrogates(pool)
if pool is not None:
pool.close()
pool.join()
del pool
amp_data = np.zeros(g.get_spatial_dims())
for i in range(g.lats.shape[0]):
for j in range(g.lons.shape[0]):
amp_data[i, j] = np.std(g.data[:, i, j], ddof=1)
log("Analysis on data done. Saving file...")
## save file in case something will go wrong with surrogates..
# from variance to standard deviation
# if ECA:
for i in range(NUM_SURR):
jobQ.put(1)
for i in range(WORKERS):
jobQ.put(None)
a = g_surrs.get_seasonality(DETREND=True)
sg = SurrogateField()
sg.copy_field(g_surrs)
if AMPLITUDE:
a_amp = g_surrs_amp.get_seasonality(True)
sg_amp = SurrogateField()
sg_amp.copy_field(g_surrs_amp)
else:
sg_amp = None
a_amp = None
if SURR_TYPE == "AR":
sg.prepare_AR_surrogates()
if AMPLITUDE:
sg_amp.prepare_AR_surrogates()
workers = [
Process(
target=_cond_difference_surrogates,
args=(sg, sg_amp, g_surrs, a, a_amp, start_cut, jobQ, resQ, ndx_season),
)
for iota in range(WORKERS)
]
for w in workers:
w.start()
tot = 0
while surr_completed < NUM_SURR:
# get result
diff, meanVar, cond_means_surr, maxmin = resQ.get()
job_args = [ (i, j, phase_data[:, i, j], amp_data[:, i, j], phase_bins) for i in range(g.lats.shape[0]) for j in range(g.lons.shape[0]) ]
else:
job_args = [ (i, j, phase_data[:, i, j], g.data[:, i, j], phase_bins) for i in range(g.lats.shape[0]) for j in range(g.lons.shape[0]) ]
job_result = map_func(_get_cond_means, job_args)
del job_args, phase_data
# map results
for i, j, cmm, cmv in job_result:
bins_data[i, j, :] = cmm
bins_data_var[i, j, :] = cmv
del job_result
# if surrogate type is AR, exploit the pool to prepare the AR model
if SURR_TYPE == 'AR' or SURR_TYPE == 'ALL':
log("Creating AR(1) model and computing residuals...")
sg.prepare_AR_surrogates(pool)
if pool is not None:
pool.close()
pool.join()
del pool
amp_data = np.zeros(g.get_spatial_dims())
for i in range(g.lats.shape[0]):
for j in range(g.lons.shape[0]):
amp_data[i, j] = np.std(g.data[:, i, j], ddof = 1)
log("Analysis on data done. Saving file...")
## save file in case something will go wrong with surrogates..
# from variance to standard deviation
# if ECA:
