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])