nrows = int(nt / tw / ncols)
testsize = 0.3
seed = 20180319
channel = 1
length = int(tw / tinc)
classes = 2
cbar_binary = ListedColormap(["darkgray", "yellow"])
s1_labels, labels_attr = f.gather_to_label(s1, tw, tinc, thre=0.2)
#np.savetxt("out/micro_labels_win10.csv", s1_labels, delimiter=',')
s1_labels = pd.read_csv("out/micro_labels_win10.csv", header=None)
s1_labels = np.array(s1_labels)
fig, ax = f.labels_plot(s1_labels)
ax.set_yticks(np.arange(0, int(nt / tw) + 1, 5) - 0.5)
ax.set_yticklabels(((np.arange(0,
int(nt / tw) + 1, 5) - 0.5) * tw + tw / 2) * dt)
ax.set_ylabel('Time (s)', fontsize=13)
#fig.savefig('fig/micro_labels.pdf', dpi=200)
#%% training dataset for MLP, CNN, and CWT-CNN
traces_step = 4
traces_train = np.arange(0, s1.shape[1], traces_step)
X = np.zeros((int(len(traces_train) * nt / tw), int(tw / tinc)))
Xcwt = np.zeros(
(int(len(traces_train) * nt / tw), len(freq_index), int(tw / tinc)))
Y = np.zeros((int(len(traces_train) * nt / tw), ))
#%% plot the gather and labels
'''generate labels for gather'''
#f.gather_plot(Obs, tw)
#Obs_labels, labels_attr_rms = f.gather_to_label(Obs, tw, tinc, thre=0.035)
#Obs_labels = labels
#np.savetxt("out/Obs_labels_win50.csv", Obs_labels, delimiter=',')
Obs_labels = pd.read_csv("out/Obs_labels_win50_m.csv", header=None)
Obs_labels = np.array(Obs_labels)
#f.gather_plot(Obs, tw, savepath='fig/gather_labels.png',
# plot_label=True, labels=Obs_labels)
fig, ax = f.labels_plot(Obs_labels)
ax.set_yticks(np.arange(0, nt / tw + 1, 400 / tw))
ax.set_yticklabels(np.arange(0, nt + 1, 400) * dt)
ax.set_xticks(np.arange(0, 967, 100))
ax.set_xticklabels((np.arange(0, 967, 100) / 10).astype(int))
#fig.savefig('fig/obs_label', dpi=200)
#%% training dataset for MLP, CNN, and CWT-CNN
traces_step = 80
traces_train = np.arange(0, 967, traces_step)
X = np.zeros((int(len(traces_train) * nt / tw), int(tw / tinc)))
Xcwt = np.zeros((int(len(traces_train) * nt / tw), 15, int(tw / tinc)))
Y = np.zeros((int(len(traces_train) * nt / tw), ))
for i in traces_train:
itrace = Obs[:, i]