er_w_time_array_A[:,i] = pt.make_slip_rate_w_time(initA, accel, erA1, erA2,
time_vector = time_vector)
er_w_time_array_B[:,i] = pt.make_slip_rate_w_time(initB, accel, erB1, erB2,
time_vector = time_vector)
cum_ext_w_time_array_A[:,i] = pt.get_cum_vector(er_w_time_array_A[:,i],
time_step)
cum_ext_w_time_array_B[:,i] = pt.get_cum_vector(er_w_time_array_B[:,i],
time_step)
er_w_time_array = er_w_time_array_A + er_w_time_array_B
cum_ext_w_time_array = cum_ext_w_time_array_A + cum_ext_w_time_array_B
# make plots
fig1 = plt.figure(1)
pt.make_fault_histograms(fig1, filtered_runs, initA_col, srA1_col, accel_col,
srA2_col)
fig2 = plt.figure(2)
pt.make_fault_histograms(fig2, filtered_runs, initB_col, srB1_col, accel_col,
srB2_col)
fig3 = plt.figure(3)
pt.make_ext_histories(fig3, 'nlrT4', time_vector, er_w_time_array,
cum_ext_w_time_array)
decimals = 3)
times = len(time_vector)
er_w_time_array = nmp.zeros((times, num_filtered))
cum_ext_w_time_array = nmp.zeros((times, num_filtered))
for i in range(num_filtered):
init = filtered_runs[i,init_col]
accel = filtered_runs[i,accel_col]
er1 = filtered_runs[i,sr1_col] * nmp.cos(fault_dip)
er2 = filtered_runs[i,sr2_col] * nmp.cos(fault_dip)
er_w_time_array[:,i] = pt.make_slip_rate_w_time(init, accel, er1, er2,
time_vector = time_vector)
cum_ext_w_time_array[:,i] = pt.get_cum_vector(er_w_time_array[:,i],
time_step)
fig1 = plt.figure(1)
pt.make_fault_histograms(fig1, filtered_runs, init_col, sr1_col, accel_col,
sr2_col)
fig2 = plt.figure(2)
pt.make_ext_histories(fig2, 'nlrT3', time_vector, er_w_time_array,
cum_ext_w_time_array)
plt.show()