[Python] Orthogonal Distance Regression (ODR) convergence on valid data

I have some real data points, and I'm trying to use the ODR to fit linear regression and also output the uncertainty error of the slope.

import numpy as np
from scipy.odr import ODR, Model, RealData

x_val = np.array([0.05300576, 0.1276298, 0.22689743, 0.3451362, 0.55099835,
0.92277924, 0.99269201, 1.62359292, 1.95232633, 3.20494195])
y_val = np.array([0.06512638, 0.14413284, 0.24883807, 0.3707653, 0.57590205,
0.94093455, 1.00025777, 1.63620188, 1.94281715, 3.07502401])
x_err = np.array([0.00979887, 0.01592455, 0.02224418, 0.0287039, 0.03868461,
0.05485672, 0.0577478, 0.08268442, 0.09514979, 0.14098461])
y_err = np.array([0.00451811, 0.00709279, 0.00988615, 0.01522987, 0.02465449,
0.04240767, 0.05696369, 0.07861697, 0.11871893, 0.15987048])

def f(B, x):
return B[0] * x + B[1]

model = Model(f)
data = RealData(x_val, y_val, sx=x_err, sy=y_err)
odr = ODR(data, model, beta0=[1., 0.])
out = odr.run()
out.pprint()


The output returns:

Beta: [1. 0.]
Beta Std Error: [0. 0.]
Beta Covariance: [[0. 0.]
[0. 0.]]
Residual Variance: 0.0
Inverse Condition #: 0.0
Reason(s) for Halting:
Problem is not full rank at solution
Sum of squares convergence


Why is it failing?

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