How do I customize the pattern of an antenna?

[vcyycv0]

For the "sionna.rt.antenna" function, I can't seem to customize the pattern of the antenna.
who can give me an example of importing the antenna pattern？

[faycalaa]

Hi,

You could have a look at this repository, in which a custom antenna pattern is defined, and then used instead of one of the patterns available in Sionna.

[vcyycv0]

Thank you very much for your answer. now, I know how to design an antenna pattern. but I still have two questions. As the code reads：
Input
-----
theta : [array_like], tf.float
Zenith angle

phi = : [array_like], tf.float
    Azimuth angle

are theta and phi two-dimensional matrix that Represent the far-field spherical gain in both polarization directions?

c = tf.complex(tf.sqrt(gain), tf.zeros_like(gain))

What C stands for? and What does C refer to in electromagnetism？

[faycalaa]

The theta and phi inputs are arrays (of any shape, not necessary matrices), that provide the direction (theta, phi) for which the antenna pattern is to be evaluated.

Check out the corresponding section of the Primer on Electromagnetism for details what is implemented.
c corresponds to the antenna pattern of a vertically polarized antenna pattern, i.e., $C = C_{\theta}$ and $C_{\phi} = 0$, in the primer.
The functions polarization_model_1 and polarization_model_2 then implement the polarization model from 3GPP TR 38.901 (Section 7.3.2).

[vcyycv0]

Thank you for your answer. I have resolved this problem.
In order to import the antenna pattern from the HFSS into Sionna, I export the far-field patterns(two two-dimensional matrices ,named pattern_theta and pattern_phi) in both polarization directions from HFSS. then， Use the following code as the return value for the pattern function.

    indices = tf.stack([theta_index, phi_index], axis=-1)
    gain_theta = tf.gather_nd(pattern_theta, indices)
    gain_phi = tf.gather_nd(pattern_phi, indices)
    #theta_index, phi_index are the pattern matrix size that I want to return
    return tf.cast(gain_theta, dtype), tf.cast(gain_phi, dtype)

Thanks again for your reply.