This didn't make any sense for what I've been trying to do (a histogram), but might be useful someday:
y, binEdges = np.histogram(vel_22,bins=15)
errArray = np.zeros((y.shape))
for i, edge in enumerate(binEdges):
vel_lo = np.where((vel_22 <= edge) & (vel_22 > binEdges[i-1]))[0]
print np.mean(vel_err[vel_lo]), 'mean'
if math.isnan(np.mean(vel_err[vel_lo])):
errArray[i -1] = 0.
else:
errArray[i -1] = np.mean(vel_err[vel_lo])
width = 12
bincenters = 0.5*(binEdges[1:]+binEdges[:-1])
plt.bar(bincenters, y, color='white', edgecolor='k', width=width, yerr=errArray)
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