gamma = params.gamma = 3.0
omega = params.omega = 0.001
params.collapse = False
metric = 'nuc'
# make file identifiers
base_name, name_mod = test_flu.get_fname(params)
#remove year
base_name = '_'.join(base_name.split('_')[:1] + base_name.split('_')[2:])
base_name = base_name.replace('_????', '')
# load data (with Koel boost and without), save in dictionary
prediction_distances = {}
normed_distances = {}
for boost in [0.0, 0.5, 1.0]:
params.boost = boost
years, tmp_pred, tmp_normed = AU.load_prediction_data(params, metric)
prediction_distances.update(tmp_pred)
normed_distances.update(tmp_normed)
##################################################################################
## main figure 3c
##################################################################################
# make figure
plt.figure(figsize=(12, 6))
# plot line for random expection
plt.plot([min(years) - 0.5, max(years) + 0.5], [1, 1], lw=2, c='k')
# add shaded boxes and optimal and L&L predictions
for yi, year in enumerate(years):
plt.gca().add_patch(
plt.Rectangle([year - 0.5, 0.2],
plt.savefig(figure_folder + 'Fig4_s2_' + base_name + '_polarizer_revised' +
ff)
##################################################################################
## Fig 5: compare bootstrap distributions of prediction results
## Bootstrapping is over years
##
##################################################################################
# load fitness prediction data
params.boost = 0.0
params.gamma = 3.0
params.omega = 0.1
params.diffusion = 0.5
params.collapse = False
years_I, prediction_distances_I, normed_distances_I = AU.load_prediction_data(
params, metric)
plotted_methods = {
m: normed_distances_I[m]
for m in [('expansion, internal nodes', 0.0,
'growth'), ('L&L', 0.0,
r'L\&L'), ('ladder rank', 0.0, 'ladder rank')]
}
ti_ext_normed = tau_i + 2
ti_int_normed = tau_i + 2 + len(mem_time_scale)
plotted_methods.update({
('polarizer', tau, 'external'):
(normed_distance[:, ti_ext_normed].mean(),
AU.boot_strap(normed_distance[:, ti_ext_normed], n=1000)),
('polarizer', tau, 'internal'):
gamma = params.gamma = 3.0
omega = params.omega = 0.001
params.collapse = False
metric = 'nuc'
# make file identifiers
base_name, name_mod = test_flu.get_fname(params)
#remove year
base_name = '_'.join(base_name.split('_')[:1]+base_name.split('_')[2:])
base_name = base_name.replace('_????','')
# load data (with Koel boost and without), save in dictionary
prediction_distances={}
normed_distances={}
for boost in [0.0,0.5,1.0]:
params.boost = boost
years,tmp_pred, tmp_normed = AU.load_prediction_data(params, metric)
prediction_distances.update(tmp_pred)
normed_distances.update(tmp_normed)
##################################################################################
## main figure 3c
##################################################################################
# make figure
plt.figure(figsize = (12,6))
# plot line for random expection
plt.plot([min(years)-0.5,max(years)+0.5], [1,1], lw=2, c='k')
# add shaded boxes and optimal and L&L predictions
for yi,year in enumerate(years):
plt.gca().add_patch(plt.Rectangle([year-0.5, 0.2], 1.0, 1.8, color='k', alpha=0.05*(1+np.mod(year,2))))
plt.plot([year-0.5, year+0.5], [prediction_distances[('minimal',boost,'minimal')][yi],
##################################################################################
## Fig 5: compare bootstrap distributions of prediction results
## Bootstrapping is over years
##
##################################################################################
# load fitness prediction data
params.boost = 0.0
params.gamma = 3.0
params.omega = 0.1
params.diffusion = 0.5
params.collapse=False
years_I,prediction_distances_I, normed_distances_I = AU.load_prediction_data(params, metric)
plotted_methods = {m:normed_distances_I[m] for m in [('expansion, internal nodes', 0.0, 'growth'),
('L&L', 0.0, r'L\&L'),
('ladder rank',0.0, 'ladder rank')]}
ti_ext_normed = tau_i+2
ti_int_normed = tau_i+2+len(mem_time_scale)
plotted_methods.update({('polarizer',tau,'external'):(normed_distance[:,ti_ext_normed].mean(), AU.boot_strap(normed_distance[:,ti_ext_normed], n=1000)),
('polarizer',tau,'internal'):(normed_distance[:,ti_int_normed].mean(),AU.boot_strap(normed_distance[:,ti_int_normed], n=1000))})
tick_labels = { ('fitness,internal nodes', 0.0, 'pred(I)'):'internal',
('fitness,terminal nodes', 0.0, 'pred(T)'):'terminal',
('expansion, internal nodes', 0.0, 'growth'):'growth',
('L&L', 0.0, r'L\&L'):r'L\&L',
('ladder rank',0.0, 'ladder rank'):'ladder rank',
('polarizer',tau,'external'):r'terminal $\tau='+str(tau)+'$',
