from experiment_class import Experiment max_doses = [200] curve_types = ['linear'] experiment_type = 'rate-survival' n_sims = 10 slopes = [.1,.5,1.5] # scale = 5 # scale time by two mut_rate = 0.00005 death_rate = 0.3 # mut_rate = mut_rate/scale # death_rate = death_rate/scale options = {'mut_rate':mut_rate, 'death_rate':death_rate, # 'x_lim':100, # 'y_lim':2*10**4, # 'counts_log_scale':True, 'plot':True} e = Experiment(max_doses=max_doses, slopes=slopes, curve_types = curve_types, experiment_type = experiment_type, n_sims=n_sims, population_options=options) e.run_experiment() e.plot_barchart()
from experiment_class import Experiment import seaborn as sns import matplotlib.pyplot as plt # max_doses = [100,300,400,500] max_doses = [350] experiment_type = 'dose-entropy' n_sims = 100 e1 = Experiment(max_doses=max_doses, experiment_type=experiment_type, n_sims=n_sims) e1.run_experiment() e = e1.entropy_results # fig,ax = plt.subplots() # sns.swarmplot(x='dose',y='max entropy',data=e,ax=ax,hue='survive condition',dodge=True,color='black') # sns.boxplot(x='dose',y='max entropy',data=e,ax=ax,hue='survive condition',dodge=True,palette='Set2') # handles, labels = ax.get_legend_handles_labels() # ax.legend(handles[:2], labels[:2])