def get_our_costs_at_Rihn(costs_Rihn, costs_ours, data, aa_mutation_rates):
'''Get our costs at their positions'''
c = costs_ours.set_index('pos').loc[costs_Rihn['pos']]['median']
costs_Rihn_by_pos = costs_Rihn.set_index('pos')
c_float = []
c_IQD_target_specfic = []
for (p, mut), ci in zip(costs_Rihn_by_pos.iterrows(), c):
if ci == '<0.001':
ci = 0
elif ci == '>0.1':
ci = 1
else:
ci = float(ci)
c_float.append(ci)
cons, ipos, target_aa = mut['mut'][0], int(mut['mut'][1:-1]), mut['mut'][-1]
ipos +=714
print(cons, mut['NL4-3'], translate(data['init_codon']['pol']['p2'][ipos]))
c_IQD_target_specfic.append(fitness_cost_mutation('pol', data,
aa_mutation_rates, ipos,
target_aa, nbootstraps=100))
c[:] = c_float
c_IQD_target_specfic = np.array(c_IQD_target_specfic)
comp = (pd.concat([c, costs_Rihn.set_index('pos')['cost']], axis=1)
.rename(columns={'median': 'ours', 'cost': 'Rihn'}))
return comp, c_IQD_target_specfic
def load_other_experiments(data, aa_mutation_rates):
fc = pd.read_csv('../data/fitness_costs_experiments.csv')
coefficients = {}
for ii, mut in fc.iterrows():
region = mut['region']
offset = offsets[mut['feature']]
aa, pos = mut['mutation'][-1], int(mut['mutation'][1:-1])+offset
coefficients[(mut['feature'], mut['mutation'])] = (mut['normalized'],
fitness_cost_mutation(region, data, aa_mutation_rates, pos, aa, nbootstraps=100))
return coefficients
