Scipy.Odr multiple variable regression

I would like to perform a multidimensional ODR with scipy.odr. I read the API documentation, it says that multi-dimensionality is possible, but I cannot make it work. I cannot find working example on the internet and API is really crude and give no hints how to proceed.

Here is my MWE:

import numpy as np
import scipy.odr

def linfit(beta, x):
    return beta[0]*x[:,0] + beta[1]*x[:,1] + beta[2]

n = 1000
t = np.linspace(0, 1, n)
x = np.full((n, 2), float('nan'))
x[:,0] = 2.5*np.sin(2*np.pi*6*t)+4
x[:,1] = 0.5*np.sin(2*np.pi*7*t + np.pi/3)+2
e = 0.25*np.random.randn(n)
y = 3*x[:,0] + 4*x[:,1] + 5 + e

print(x.shape)
print(y.shape)

linmod = scipy.odr.Model(linfit)
data = scipy.odr.Data(x, y)
odrfit = scipy.odr.ODR(data, linmod, beta0=[1., 1., 1.])
odrres = odrfit.run()
odrres.pprint()

It raises the following exception:

scipy.odr.odrpack.odr_error: number of observations do not match

Which seems to be related to my matrix shapes, but I do not know how must I shape it properly. Does anyone know?

Firstly, in my experience scipy.odr uses mostly arrays, not matrices. The library seems to make a large amount of size checks along the way and getting it to work with multiple variables seems to be quite troublesome.

This is the workflow how I usually get it to work (and worked at least on python 2.7):

import numpy as np
import scipy.odr

n = 1000
t = np.linspace(0, 1, n)

def linfit(beta, x):
    return beta[0]*x[0] + beta[1]*x[1] + beta[2] #notice changed indices for x

x1 = 2.5*np.sin(2*np.pi*6*t)+4
x2 = 0.5*np.sin(2*np.pi*7*t + np.pi/3)+2

x = np.row_stack( (x1, x2) ) #odr doesn't seem to work with column_stack

e = 0.25*np.random.randn(n)
y = 3*x[0] + 4*x[1] + 5 + e #indices changed

linmod = scipy.odr.Model(linfit)
data = scipy.odr.Data(x, y)
odrfit = scipy.odr.ODR(data, linmod, beta0=[1., 1., 1.])
odrres = odrfit.run()
odrres.pprint()

So using identical (1D?) arrays, using row_stack and adressing by single index number seems to work.