def plot_sudden_markov_fixed_decision_mat(input_fname, plot_fname, label):
"""
Plot decision matrix for sudden switch Markov model, with
FIXED growth rates for glucose and galactose.
"""
print "plotting: %s" %(os.path.basename(plot_fname))
plt.figure(figsize=(5, 4))
params = simulation.load_params(MARKOV_PARAM_FNAME)
params["gluc_growth_rate"] = 0.3
params["galac_growth_rate"] = 0.075
model_sudden_markov.plot_decision_mat(params)
plt.tight_layout()
plt.savefig(plot_fname)
def plot_sudden_markov_variable_decision_mat(input_fname, plot_fname, label):
"""
Plot decision matrix for sudden switch Markov model, with
VARIABLE growth rates for glucose and galactose.
"""
print "plotting: %s" %(os.path.basename(plot_fname))
fig = plt.figure(figsize=(6, 3.1))
params = simulation.load_params(MARKOV_PARAM_FNAME)
params["gluc_growth_rate"] = 0.3
# 4-fold lower growth rate, 2-fold, nearly equal growth rates
galac_growth_rates = [0.075, 0.15, 0.28]
num_plots = len(galac_growth_rates)
# set heatmap aspect equal
plt.gca().set_aspect("equal")
sns.set_style("white")
for plot_num, galac_growth_rate in enumerate(galac_growth_rates):
show_x_label = False
show_y_label = False
show_colorbar_label = False
params["galac_growth_rate"] = galac_growth_rate
plt.subplot(1, num_plots, plot_num + 1)
if (plot_num + 1) == 1:
show_x_label = False
show_y_label = True
elif (plot_num + 1) == 2:
show_x_label = True
show_y_label = False
elif (plot_num + 1) == 3:
show_x_label = False
show_colorbar_label = True
model_sudden_markov.plot_decision_mat(params,
show_x_label=show_x_label,
show_y_label=show_y_label,
show_colorbar_label=show_colorbar_label)
plt.subplots_adjust(wspace=0.5, left=0.1)
fig.set_tight_layout(True)
plt.savefig(plot_fname)
