def read_qrnn(qrnn_file, test_file, inChannels, target):
data = mwhsData(test_file, inChannels, target, ocean=False, test_data=True)
qrnn = QRNN.load(qrnn_file)
y_pre, y_prior, y0, y, y_pos_mean = S.predict(data, qrnn, \
add_noise = False)
return y_pre, y_prior, y0, y, y_pos_mean
#%% read input data
qrnn_path = os.path.expanduser("~/Dendrite/Projects/AWS-325GHz/MWHS/qrnn_output/all_with_flag/%s/"%(qrnn_dir))
inChannels = np.concatenate([[target], channels])
# inChannels = np.array(channels)
print(qrnn_dir, channels, inChannels)
qrnn_file = os.path.join(qrnn_path, "qrnn_mwhs_%s.nc"%(target))
print (qrnn_file)
i183, = np.argwhere(inChannels == target)[0]
data = mwhsData(test_file,
inChannels, target, ocean = False, test_data = True)
qrnn = QRNN.load(qrnn_file)
y_pre, y_prior, y0, y, y_pos_mean, x = predict(data, qrnn, \
add_noise = False)
im = (np.abs(y_pre[:, 3] - y_prior[:, i183] )< 5)
# bia, std, ske, mae = S.calculate_statistics(y_prior, y0, y, y_pre[:, 3], im, i183)
SI = np.abs(y_prior[:, 0] - y_prior[:, 1])
im = SI < 5.0
y_pre = y_prior[im, i183]
y0 = y0[im]
y = y[im]