# get run data for particular parameter value
Dt = 0
run_data = get_run_data_from_sweep_data(sweep_data, Dt)
# setup figure
ax = setup_trajectory_figure(Dt / 60. + 2)
ax[0].set_title(species + ' - ' + model + ', Dt: %02d min, T_dec>%02d min' %
(Dt, thr_Tdec))
# select single (or multiple) trajectory index
#np.random.seed(20)
idx = np.random.randint(0, len(run_data), 1)
idx = np.arange(len(run_data))
# for the selected trajectories
for i in tqdm(idx):
# get species data
(t, ts) = get_species_data_from_sim_data(run_data[i]['sim_data'], species,
save_species_list)
# get T_decision
(AC_id, T_dec, ind) = get_T_decision(t, ts, 0.3)
if T_dec > thr_Tdec:
# calculate and plot difference in D_i(t) for all three stages
plot_single_trajectory(t, ts, ax, thr=0.3)
# print('T_decision for trajectory %03d: %.4f'%(i,T_dec/60.))
plt.show()
def plot_Tdec(model, color, fig, species):
# sweep parameters
save_species_list = ['M', 'D', 'MY', 'DY']
sweep_data_dir = model + '_sweep'
sweep_data_file = 'sweep'
sweep_param_lbl = 'DT'
sweep_param_list = [0., 10., 20., 30., 40., 50., 60., 70., 80., 90.]
### analyze sweep data
# load sweep data
infile = os.path.join(os.getcwd(), sweep_data_dir,
sweep_data_file + '_' + sweep_param_lbl + '.p')
sweep_data = pickle.load(open(infile, "rb"))
# initialize matrix for AC_id and T_dec
AC_ids = np.array([np.zeros((len(i['run_data']))) for i in sweep_data])
T_decs = np.array([np.zeros((len(i['run_data']))) for i in sweep_data])
# for each sweep_parameter
for i, sweep_param in enumerate(sweep_param_list):
print(sweep_param)
# get run data for particular parameter value
run_data = get_run_data_from_sweep_data(sweep_data, sweep_param)
# extract AC_id and T_dec
# for each simulation in run_data
for j in range(0, len(run_data)):
# get species data
(t, ts) = get_species_data_from_sim_data(run_data[j]['sim_data'],
species,
save_species_list)
# print(t)
# break
# get T_decision
(AC_id, T_dec, ind) = get_T_decision(t, ts, 0.3)
# print(AC_id, T_dec)
AC_ids[i, j] = AC_id
T_decs[i, j] = T_dec
frac = np.array([np.sum(AC_id) / AC_id.shape[0] for AC_id in AC_ids])
mean_T_dec = np.array([np.mean(T_dec) for T_dec in T_decs])
std_T_dec = np.array([np.std(T_dec) for T_dec in T_decs])
print(mean_T_dec)
print(np.sum(np.array(T_decs[0]) > 30) / len(T_decs[0]))
print(T_decs.shape)
[ax1, ax2, ax3] = fig.get_axes()
ax1line = ax1.plot(sweep_param_list, frac, '-o', ms=3, color=color, lw=0.5)
ax2line = ax2.plot(sweep_param_list,
mean_T_dec,
'-o',
ms=3,
color=color,
lw=0.5)
ax3line = ax3.hist(
T_decs[0],
bins=48,
range=[0, 480],
orientation='horizontal',
weights=[1. / len(T_decs[0]) for i in np.arange(len(T_decs[0]))],
color=color,
alpha=.5)
return ax1line, ax2line, ax3line
sweep_data = pickle.load(open(infile, "rb"))
# get run data for particular parameter value
run_data = get_run_data_from_sweep_data(sweep_data, 20)
# plot dynamics and T_dec
col = 'kcm'
# for each simulation in run_data
for i in range(0, len(run_data))[::10]:
# get species data
(t, D_vs_t) = get_species_data_from_sim_data(run_data[i]['sim_data'], 'D',
save_species_list)
# calculate and plot difference in D_i(t) for all three stages
diff_D = []
for j in [0, 1, 2]:
diff_D.append((D_vs_t[j][:, 0] - D_vs_t[j][:, 1]) /
(D_vs_t[j][:, 0] + D_vs_t[j][:, 1]))
plt.plot(t[j] / 60., diff_D[j], '-' + col[j], lw=2)
# get T_decision
(AC_id, T_dec, ind) = get_T_decision(t, D_vs_t, 0.5)
# and plot in figure
if len(ind) > 0:
plt.plot(t[ind[0]][ind[1]] / 60., diff_D[ind[0]][ind[1]], 'ok')
plt.plot([0, 12], [0, 0], '--k')
plt.ylim([-1.1, 1.1])
plt.xlabel('Time (hours)')
plt.ylabel('Normalized difference')
plt.show()
fig, ax = plt.subplots(figsize=(5, 5), nrows=1, ncols=1)
fig.suptitle(model + '- T_dec correlation')
# select single (or multiple) trajectory index
#np.random.seed(20)
idx = np.random.randint(0, len(run_data), 1)
idx = np.arange(len(run_data))
# for the selected trajectories
T_dec = [[], []]
for i in tqdm(idx):
# get species data
(t, ts) = get_species_data_from_sim_data(run_data[i]['sim_data'], 'D',
save_species_list)
# get T_decision
(AC_id, td, ind) = get_T_decision(t, ts, 0.3)
T_dec[0].append(td)
# get species data
(t, ts) = get_species_data_from_sim_data(run_data[i]['sim_data'], 'DY',
save_species_list)
# get T_decision
(AC_id, td, ind) = get_T_decision(t, ts, 0.3)
T_dec[1].append(td)
ax.plot(T_dec[0], T_dec[1], 'ok')
ax.set_ylim(0, 360)
ax.set_xlim(0, 360)
ax.set_xlabel('T_dec with LAG2 (min)')
ax.set_ylabel('T_dec with YFP (min)')
plt.show()