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()