Why does my new data with length of 12, give me 48 posterior predictive observations?

Questions v5
5

Thank you. What does t_ = pm.MutableData('t', t) become in terms of type of object when I define them inside the model?

When I try to get len(t_), I get an error

TypeError: object of type 'TensorSharedVariable' has no len()

But I need the length for these parts of the code:

yearly_beta = pm.Normal('yearly_beta', 0, 1, shape = n_components*2)
    yearly_seasonality = pm.Deterministic('yearly_seasonality',at.dot(yearly_X(t_, 365.25/**len(t_)**), yearly_beta))
    
    monthly_beta = pm.Normal('monthly_beta', 0, 1, shape = monthly_n_components*2)
    monthly_seasonality = pm.Deterministic('monthly_seasonality',at.dot(monthly_X(t_, 30.5/**len(t_)**), monthly_beta))

UPDATE:
I don’t think it’s the len. I did the following:
len_t = pm.ConstantData('len_t', len(t)). Then I got the following error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_5309/1167067047.py in <module>
     16 
     17     yearly_beta = pm.Normal('yearly_beta', 0, 1, shape = n_components*2)
---> 18     yearly_seasonality = pm.Deterministic('yearly_seasonality',at.dot(yearly_X(t_, 365.25/len(t)), yearly_beta))
     19 
     20     monthly_beta = pm.Normal('monthly_beta', 0, 1, shape = monthly_n_components*2)

/tmp/ipykernel_5309/3136029572.py in yearly_X(t, p, n)
      9 def yearly_X(t, p=365.25, n=n_components):
     10     x = 2 * np.pi * (np.arange(n)+1) * t[:, None] / p
---> 11     return np.concatenate((np.cos(x), np.sin(x)), axis = 1)
     12 
     13 monthly_n_components = 5

<__array_function__ internals> in concatenate(*args, **kwargs)

ValueError: zero-dimensional arrays cannot be concatenated

I think my fourier_seasonality function needs to be rewritten in aesara to be used with pm.Data object?