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)