Hello all,

I was trying to model the error in an AR(1) process but was having trouble with the mechanics of implementing the scan function.

I am using aesara version 2.6.6. I have also tried it on google colabs and I get the same error in colabs.

I generated some fake data and tried to use aesara scan to implement the error

Here is my sample code:

```
#Code to look at how to model AR(1) in PyMC
# AR(1) Process:
# y_t = mu + phi * (y_t-1) + eps_t
# Where eps_t = Normal(0, 1)
# Import libraries
import numpy as np
import aesara
import aesara.tensor as at
import pymc as pm
import matplotlib.pyplot as plt
import arviz as az
# Set style
az.style.use('arviz-darkgrid')
# Set Random Seed
RANDOM_SEED = 1234
# Generate the data
# Inputs
Npts = 100
mu_sim = 10.0 # sim stands for simulation
phi_sim = 0.55 # Autoregressive coefficient
# Initialize the data
y_sim = np.zeros(Npts)
y_sim[0] = mu_sim
# Generate the fake noise
np.random.seed(RANDOM_SEED)
eps_sim = np.random.normal(0, 1, size = Npts - 1) # Create size of N - 1
eps_sim = np.concatenate([[0], eps_sim]) # Then concatenate [0] with the above step
for i in range(1, Npts):
y_sim[i] = mu_sim + phi_sim * y_sim[i - 1] + eps_sim[i]
# Calculate the error using aesara scan
def ar_1_error(y_tm1, y_t, mu, phi):
'''
----------------------------------------
Calculate the error from a AR(1) process
error_t = y_t - mu - phi * y_tm1
---------------------------------------
Inputs:
y_tm1: lagged 1 value of time series
y_t: current time series value
mu: mean
phi: AR(1) coefficient
Returns:
error_t = y_t - mu - phi * y_tm1
'''
error_t = y_t - mu - phi * y_tm1
return error_t
err, _ = aesara.scan(
fn = ar_1_error,
sequences = dict(input = y_sim, taps = [-1, 0]),
non_sequences = [mu_sim, phi_sim]
)
```

I get the following error:

**---------------------------------------------------------------------------** **TypeError** Traceback (most recent call last) **c:\Users\Prabh\OneDrive\Desktop\error_AR.ipynb Cell 1** in <cell line: 120>**()** 116 error_t = y_t - mu - phi * y_tm1 118 return error_t ** โ 120** err, _ = aesara.scan( 121 fn = ar_1_error, 122 sequences = dict(input = y_sim, taps = [-1, 0]), 123 non_sequences = [mu_sim, phi_sim] 124 ) File **c:\Users\Prabh\Anaconda3\envs\intuitive_bayes\lib\site-packages\aesara\scan\basic.py:681**, in scan**(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile, allow_gc, strict, return_list)** 678 else: 679 actual_n_steps = at.as_tensor(n_steps) ** โ 681** scan_seqs = [seq[:actual_n_steps] for seq in scan_seqs] 682 # Conventions : 683 # mit_mot = multiple input taps, multiple output taps ( only provided 684 # by the gradient function ) **(โฆ)** 688 689 # MIT_MOT โ not provided by the user only by the grad function 690 n_mit_mot = 0 File **c:\Users\Prabh\Anaconda3\envs\intuitive_bayes\lib\site-packages\aesara\scan\basic.py:681**, in **(.0)** 678 else:

โฆ

688

689 # MIT_MOT โ not provided by the user only by the grad function

690 n_mit_mot = 0

TypeError: slice indices must be integers or None or have an **index** method

Thanks!