def percentile_function(x):

lower = np.percentile(x,percentiles[0])

median = np.median(x)

upper = np.percentile(x,percentiles[1])

return lower, median, upper

if ranges == None:

ranges = (np.min(x),np.max(x),np.min(y),np.max(y))

if isinstance(bins, int) == True:

bins = np.linspace(ranges[0],ranges[1],bins+1)

xbin_centres = [bins[i]+(bins[i+1]-bins[i])/2 for i in range(len(bins)-1)]

x_bins, _, bin_assignment = binned_statistic(x,x,bins=bins,statistic='median')

x_medians = np.array([])

x_uppers = np.array([])

x_lowers = np.array([])

y_medians = np.array([])

y_uppers = np.array([])

y_lowers = np.array([])

for b in np.unique(bin_assignment):

in_bin = bin_assignment == b

x_bin = x[in_bin]

y_bin = y[in_bin]

if use_bootstrap == True:

x_lower, x_median, x_upper = bootstrap(x_bin,10,percentile_function,random_state=0)

y_lower, y_median, y_upper = bootstrap(y_bin,10,percentile_function,random_state=0)

else:

x_lower, x_median, x_upper = percentile_function(x_bin)

y_lower, y_median, y_upper = percentile_function(y_bin)

x_lowers = np.append(x_lowers,x_lower)

x_medians = np.append(x_medians,x_median)

x_uppers = np.append(x_uppers,x_upper)

y_lowers = np.append(y_lowers,y_lower)

y_medians = np.append(y_medians,y_median)

y_uppers = np.append(y_uppers,y_upper)

stats_table = Table()

stats_table['x'] = x_medians

stats_table['x_upper'] = x_uppers

stats_table['x_lower'] = x_lowers

stats_table['y'] = y_medians

stats_table['y_upper'] = y_uppers

stats_table['y_lower'] = y_lowers

stats_table['x_centres'] = xbin_centres

return stats_table, bins