import f_time_frequency as tf
import f
#%% microseismic data ########################################
s1 = np.array(pd.read_csv("data/micro.csv", header=None))
#fig, ax = plt.subplots(figsize=(10,8))
#ax.imshow(s1,interpolation='quadric', aspect='auto',vmax=1000,vmin=-1000)
min_max_scaler = preprocessing.MaxAbsScaler()
s1 = min_max_scaler.fit_transform(s1)
dt = 0.002
nt = s1.shape[0]
fig, ax = f.seisplot_wig(s1, lw=0.8)
ax.set_yticks(np.arange(0, nt + 1, 50))
ax.set_yticklabels(np.arange(0, nt + 1, 50) * dt)
#fig.savefig('fig/micro.pdf', dpi=200)
t = np.arange(400) * dt * 1000
cwtmatr, freqs = tf.spectrum_cwt(t,
s1[:, 13],
wavelet='morl',
widths=200,
cmap='RdBu',
colorscale=1,
contour_dvision=41,
freqmax=100,
figsize=(12, 3),
plot=True,
length = int(tw / tinc)
classes = 2
cbar_binary = ListedColormap(["darkgray", "yellow"])
tr_num = 260 # trace number
#%%
Obsfb = f.fb_pick_gather_wholetrace(Obs,
fb_tr_step=10,
method='kmeans',
w=21,
c=2)
fig, ax = f.seisplot_wig(
Obs,
inc=10,
scale=30,
lw=0.2,
)
#ax.scatter(Obsfb[:,0],Obsfb[:,1], s=30,facecolors='none',edgecolors='r',lw=1)
ax.set_yticks(np.arange(0, nt + 1, 400))
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)
#fig.savefig('fig/obs', dpi=200)
#%% samples generate for one trace
#obs = Obs[:,tr_num]
#fig, axe = plt.subplots(figsize=(12,3))
#axe.plot(np.arange(len(obs)), obs, 'k', lw=1)
#axe.set_ylabel('Amplitude', fontsize=13)