print(kd)
##############################################################################################
# Process reversible data
#kao_r = [10,100,1000,10000]
#basedir_r = './full_reversible_sims/weights_L1_S0'
#datadirs = [basedir_r+'/run_bulk_nL{}_nC{}_kOn{:.0f}_kOff1/'.format(nL,nC,kaoi) for kaoi in kao_r]
#[n_mean_r, n_var_r, surv_data, logsurv_data, event_density_data, nmean_rev] = qan.process_reversible_data(datadirs)
kao_rw05_strings = ["1E+0","1E+1","1E+2","1E+3","1E+4"]
kao_rw05 = [1E+0,1E+1,1E+2,1E+3,1E+4]
basedir_r = './full_reversible_sims/weights_L05_S05'
datadirs = [basedir_r+'/run_bulk_nL{}_nC{}_kOn{}_kOff1E-0/'.format(nL,nC,kaoi) for kaoi in kao_rw05_strings]
[n_mean_rw05, n_var_rw05, surv_dataw05, logsurv_dataw05, event_density_dataw05, nmean_revw05] = qan.process_reversible_data_v2(datadirs)
##############################################################################################
# Calculate expected occupancy for Detailed-balance reversible simulations
micro_onrates = np.logspace(0.,5.,100)
micro_offrate = kdo #kdo_r
n_db = calculate_lma_occupancy(micro_onrates,micro_offrate,V,Vex,Vreact_eff)
n_var_db = n_db * (1. - n_db)
##############################################################################################
# PLOTTING
##############################################################################################
# Plot mean activation and variance (point statistics)
figPS, axPS = plt.subplots(1,2,figsize=(10,5))
##############################################################################################
# Process reversible data
kao_r = [10, 100, 1000, 10000]
basedir_r = './full_reversible_sims/small_box_5_5_5'
datadirs = [
basedir_r +
'/run_bulk_nL{}_nC{}_kOn{:.0f}_kOff1/'.format(nL, nC, kaoi)
for kaoi in kao_r
]
[
n_mean_r, n_var_r, surv_data, logsurv_data, event_density_data,
nmean_rev
] = qan.process_reversible_data_v2(datadirs)
##############################################################################################
# Calculate expected occupancy for Detailed-balance reversible simulations
micro_onrates = np.logspace(0., 5., 100)
micro_offrate = kdo #kdo_r
n_db = calculate_lma_occupancy(micro_onrates, micro_offrate, V, Vex,
Vreact_eff)
n_var_db = n_db * (1. - n_db)
##############################################################################################
# PLOTTING
##############################################################################################
#---------------------------------------------------------------------------------------------
# Plot survival probability
traj_length = 1e7 # number of timesteps for each trajectory
#ntraj_r = [1, 5, 10, 20, 40, 80, 160]
ntraj_r = [1, 10, 30, 80]
number_trajs = [10, 10, 5, 3] # number of traj sets in ensemble for each ntraj
ntraj_tuples = [[(i*ntraji, (i+1)*ntraji) for i in range(number_trajsi)] for ntraji,number_trajsi in zip(ntraj_r,number_trajs)]
true_ntraj_r = 240
n_mean_r = [ [ [] for ntraji in ntraj_r] for kaoi in kao_r]
logsurv_data_r = [ [ [] for ntraji in ntraj_r] for kaoi in kao_r]
for i,(ntraji,tuplesi) in enumerate(zip(ntraj_r,ntraj_tuples)):
for tuplesij in tuplesi:
out_rij = qan.process_reversible_data_v2(datadirs,ntraj=tuplesij) # output for ntraj_i and tuple_set_j for all kao vals
for k in range(len(kao_r)):
n_mean_r[k][i].append(out_rij[0][k])
logsurv_data_r[k][i].append(out_rij[3][k])
true_out_r = qan.process_reversible_data_v2(datadirs,ntraj=true_ntraj_r)
true_n_mean_r = true_out_r[0]
true_logsurv_data_r = true_out_r[3]
##############################################################################################
# Calculate expected occupancy for Detailed-balance reversible simulations
micro_onrates = np.logspace(0.,5.,100)
micro_offrate = kdo #kdo_r
n_db = calculate_lma_occupancy(micro_onrates,micro_offrate,V,Vex,Vreact_eff)
preb_qr[i].append(1. - pesc)
##############################################################################################
# Process reversible data
kao_r = [10,100,1000,10000]
basedir_r = './full_reversible_sims/small_box_5_5_5'
datadirs = [basedir_r+'/run_bulk_nL{}_nC{}_kOn{}_kOff1/'.format(nL,nC,kaoi) for kaoi in kao_r]
#[n_mean_r, n_var_r, surv_data, logsurv_data, event_density_data, nmean_rev] = qan.process_reversible_data_v2(datadirs)
ntraj_r = [1, 5, 10, 20, 30, 40]
out_r = []
for ntraji in ntraj_r:
out_r.append(qan.process_reversible_data_v2(datadirs,ntraj=ntraji))
n_mean_r = [x[0] for x in out_r]
n_var_r = [x[1] for x in out_r]
surv_data_r = [x[2] for x in out_r]
logsurv_data_r = [x[3] for x in out_r]
event_density_r = [x[4] for x in out_r]
nmean_r = [x[5] for x in out_r]
##############################################################################################
# Calculate expected occupancy for Detailed-balance reversible simulations
micro_onrates = np.logspace(0.,5.,100)
micro_offrate = kdo #kdo_r
n_db = calculate_lma_occupancy(micro_onrates,micro_offrate,V,Vex,Vreact_eff)
n_var_db = n_db * (1. - n_db)