def read_qrnn(file, inChannels, target):
data = iciData(test_file, inChannels, target, batch_size=batchSize)
qrnn = QRNN.load(file)
y_pre, y_prior, y0, y, y_pos_mean = S.predict(data, qrnn, add_noise=True)
return y_pre, y_prior, y0, y, y_pos_mean
def read_qrnn(file, inChannels, target):
data = iciData(test_file, inChannels, target, batch_size=batchSize)
# read QRNN
# file = 'qrnn_ici_%s_%s_%s_single.nc'%(depth, width, target)
# print (file)
qrnn = QRNN.load(file)
y_pre, y_prior, y0, y, y_pos_mean = S.predict(data, qrnn, add_noise=True)
return y_pre, y_prior, y0, y, y_pos_mean
bins = np.arange(-20, 15, binstep)
iq = np.argwhere(quantiles == 0.5)[0,0]
#%% Uncertainty plot
plt.rcParams.update({'font.size': 26})
inChannels = np.array([target, 'I5V' , 'I6V', 'I7V', 'I8V', 'I9V', 'I10V', 'I11V'])
i183, = np.argwhere(inChannels == target)[0]
data = iciData("TB_ICI_test.nc",
inChannels, target,
batch_size = batchSize)
file = 'qrnn_ici_%s_%s_%s_single.nc'%(depth, width, target)
print (file)
qrnn = QRNN.load(file)
y_pre, y_prior, y0, y, y_pos_mean = S.predict(data, qrnn, add_noise = True)
fig, ax = plt.subplots(1, 1, figsize = [8, 8])
x = np.arange(-3, 4, 1)
ii = 0
y_all = []
randomList = random.sample(range(0, 24000), 1500)
for i in randomList:
ii +=1
#for i in ind:
y1 = y_pre[i, :] - y_pre[i, 3]
y_all.append(y1)
ax.plot(x, y1, color = colors["grey"], alpha = 0.4)
#%%
y_all = np.stack(y_all)
box1 = ax.boxplot(y_all, positions = x, showfliers=False, widths = 0.9)
for item in ['boxes', 'whiskers', 'fliers', 'medians', 'caps']:
