# for surr_num in range(NUM_SURR):
# print surr_num
# surr = nao.copy()
# surr.data = get_single_FT_surrogate(nao.data)
# surr.wavelet(8, 'y', cut = 1)
# args = [ (i, j, net.phase[:, i, j], surr.phase) for i in range(net.lats.shape[0]) for j in range(net.lons.shape[0])]
# results = pool.map(_get_MI, args)
# for i, j, res in results:
# nao_surrs[surr_num, i, j] = res
# pool.close()
# pool.join()
with open("NCEP-SAT-NAO-8yr-phase-%dFTsurrs-MIEQQ-k=32.bin" % NUM_SURR,
"rb") as f:
# cPickle.dump({'data' : nao_synch, 'surrs' : nao_surrs}, f, protocol = cPickle.HIGHEST_PROTOCOL)
raw = cPickle.load(f)
nao_synch = raw['data']
nao_surrs = raw['surrs']
result = nao_synch.copy()
p_vals = get_p_vals(nao_synch, nao_surrs, one_tailed=True)
msk = np.less_equal(p_vals, P_VAL)
result[~msk] = np.nan
fname = "NCEP-SAT-NAO-8yr-phase-%dFTsurrs-MIEQQ-k=32.png" % NUM_SURR
net.quick_render(field_to_plot=result,
tit="8yr phase synch TEMP x NAO \n p-value %.2f" % P_VAL,
symm=False,
fname=fname)
P_VAL = 0.05
# nao_surrs = np.zeros([NUM_SURR] + net.get_spatial_dims())
# for surr_num in range(NUM_SURR):
# print surr_num
# surr = nao.copy()
# surr.data = get_single_FT_surrogate(nao.data)
# surr.wavelet(8, 'y', cut = 1)
# args = [ (i, j, net.phase[:, i, j], surr.phase) for i in range(net.lats.shape[0]) for j in range(net.lons.shape[0])]
# results = pool.map(_get_MI, args)
# for i, j, res in results:
# nao_surrs[surr_num, i, j] = res
# pool.close()
# pool.join()
with open("NCEP-SAT-NAO-8yr-phase-%dFTsurrs-MIEQQ-k=32.bin" % NUM_SURR, "rb") as f:
# cPickle.dump({'data' : nao_synch, 'surrs' : nao_surrs}, f, protocol = cPickle.HIGHEST_PROTOCOL)
raw = cPickle.load(f)
nao_synch = raw['data']
nao_surrs = raw['surrs']
result = nao_synch.copy()
p_vals = get_p_vals(nao_synch, nao_surrs, one_tailed = True)
msk = np.less_equal(p_vals, P_VAL)
result[~msk] = np.nan
fname = "NCEP-SAT-NAO-8yr-phase-%dFTsurrs-MIEQQ-k=32.png" % NUM_SURR
net.quick_render(field_to_plot = result, tit = "8yr phase synch TEMP x NAO \n p-value %.2f" % P_VAL, symm = False, fname = fname)
surr_res = cPickle.load(f)
# INDICES = ['TNA', 'SOI', 'SCAND', 'PNA', 'PDO', 'EA', 'AMO', 'NAO', 'NINO3.4', 'TPI', 'SAM']
P_VAL = 0.05
data_corrs = surr_res['data']
surr_corrs = surr_res['surrs']
no_sigs = np.zeros_like(data_corrs)
msk = np.isnan(data_corrs)
no_sigs[msk] = np.nan
result = data_corrs.copy()
surrs_tmp = np.array([surr_corrs[i] for i in range(len(surr_corrs))])
p_vals = get_p_vals(data_corrs, surrs_tmp, one_tailed = False)
msk = np.less_equal(p_vals, P_VAL)
result[~msk] = np.nan
no_sigs[msk] += 1
tit = ("WeMO vs. phase fluc: JFM annual means - 1949 -- 2014 \n p-value %.2f" % (P_VAL))
fname = ("../scale-nets/WeMO-NCEP-phase-fluc-JFMmeans-1949-2014-SURR.png")
# print result
# net.data[0, ...] = result.copy()
net.quick_render(field_to_plot = result, tit = tit, fname = fname, symm = True, whole_world = True)
# tit = ("ECA&D number of significant with p-value %.2f" % (P_VAL))
# fname = ("../scale-nets/ECAD-SAT-annual-phase-fluc-SSA-RC-number-of-sig-from-indices.png")