cum_ext_w_time_array_A = nmp.zeros((times, num_filtered)) cum_ext_w_time_array_B = nmp.zeros((times, num_filtered)) for i in range(num_filtered): initA = filtered_runs[i,initA_col] initB = filtered_runs[i,initB_col] accel = filtered_runs[i,accel_col] erA1 = filtered_runs[i,srA1_col] * nmp.cos(fault_A_dip) erB1 = filtered_runs[i,srB1_col] * nmp.cos(fault_B_dip) #er1 = erA1 + erB1 erA2 = filtered_runs[i,srA2_col] * nmp.cos(fault_A_dip) erB2 = filtered_runs[i,srB2_col] * nmp.cos(fault_B_dip) #er2 = erA2 + erB2 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
time_vector = pt.make_time_vector(time_start = time_start, time_stop = time_stop, time_step = time_step, 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)