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)