Hk package#

seispy.hk module#

seispy.hk.ci(allstack, h, kappa, ev_num)[source]#

Search best H and kappa from stacked matrix. Calculate error for H and kappa :param allstack: stacked HK matrix :param h: 1-D array of H :param kappa: 1-D array of kappa :param ev_num: event number :return:

seispy.hk.hk()[source]#
seispy.hk.hk_sta_test()[source]#
seispy.hk.hksta(hpara, isplot=False, isdisplay=False)[source]#
seispy.hk.hkstack(seis, t0, dt, p, h, kappa, vp=6.3, weight=(0.7, 0.2, 0.1))[source]#
seispy.hk.hktest()[source]#
seispy.hk.plot(stack, allstack, h, kappa, besth, bestk, cvalue, cmap=<matplotlib.colors.ListedColormap object>, title=None, path=None)[source]#
seispy.hk.print_result(besth, bestk, maxhsig, maxksig, print_comment=True)[source]#
seispy.hk.time2idx(times, ti0, dt)[source]#
seispy.hk.tppps(depth, eta_p, eta_s)[source]#
seispy.hk.tps(depth, eta_p, eta_s)[source]#
seispy.hk.tpsps(depth, eta_s)[source]#
seispy.hk.transarray(array, axis=0)[source]#
seispy.hk.vslow(v, rayp)[source]#

seispy.hkpara module#

class seispy.hkpara.HKPara[source]#

Bases: object

Attributes
hrange
krange
property hrange#
property krange#
seispy.hkpara.hkpara(cfg_file)[source]#