def f90_contamination_ER(x1, x2, y1, y2, cutoff):
bins, conf = f90_confidence_mult(list(zip(x2, y2)), cutoff)
dset = list(zip(x1, y1))
contamination = []
for i in range(len(bins)):
contamination = contamination + [
len(
list(filter(lambda z: z[0] == bins[i] and z[1] < conf[i],
dset)))
]
plt.plot(bins,
contamination,
'go',
markersize=3,
label='Number of contamination points')
plt.legend(loc='best')
save_plot_custom_f90("contamination_f90_ER " + str(sum(contamination)) +
" contamination points")
plt.clf()
def f90_colored_contamination_NR(x1, x2, y1, y2, cutoff):
bins, conf = f90_confidence_mult(list(zip(x1, y1)), 1 - cutoff)
dset = list(zip(x2, y2))
contamination = []
acontamination = []
for i in range(len(bins)):
contamination = contamination + list(
filter(lambda z: z[0] == bins[i] and z[1] > conf[i], dset))
acontamination = acontamination + list(
filter(lambda z: z[0] == bins[i] and z[1] < conf[i], dset))
if contamination:
x = list(zip(*contamination))[0]
y1 = list(zip(*contamination))[1]
else:
x, y1 = [], []
if acontamination:
ax = list(zip(*acontamination))[0]
ay1 = list(zip(*acontamination))[1]
else:
ax, ay1 = [], []
plt.plot(x,
y1,
'go',
markersize=3,
label='ER in ' + str(cutoff) + ' NR confidence')
plt.plot(ax,
ay1,
'bo',
markersize=3,
label='ER outside ' + str(cutoff) + ' NR confidence')
plt.plot(bins,
conf,
'ro',
markersize=3,
label=str(cutoff) + ' NR confidence')
plt.legend(loc='best')
save_plot_custom_f90("colored contamination_f90_NR")
plt.clf()
def f90_confidence_ER(x, y2, cutoff):
y_c = f90_confidence_mult(list(zip(x, y2)), cutoff)
plt.plot(y_c[0], y_c[1], 'ro', label=str(cutoff)+" confidence", markersize=3)
plt.legend(loc='best')
save_plot_custom_f90("confidence_ER")
plt.clf()