Hey all,

Just wanted to point out that there’s a critical error in one of the hidden functions of the notebook for tutorial 3 of Bayes day, which has the potential to confuse people quite a bit. This becomes clear when you look at the first graph of brain encoded stim x tilde given true stim x:

What we see here that’s very strange, is that for values of the true stimulus that are positive, instead of a gaussian centered around the true value, we see a gaussian centered around the *negative* of Xtrue. This is of course not a very accurate approximation of the value. We can confirm that this is a graphing error by looking at the actual values of hypothetical stim and the corresponding likelihood array:

```
print(hypothetical_stim[0],x[np.argmax(likelihood_array[0,:])])
print(hypothetical_stim[-1],x[np.argmax(likelihood_array[-1,:])])
```

which returns

```
-8.0 -8.000000000000007
8.0 7.999999999999936
```

Aha, so the actual peak of our pdf *is* centered around the true value of x, despite what the graph suggests! So what’s going wrong here then? Well, the culprit is the function used to plot the graph, `plot_myarray()`

, which is hidden in the helper function cell at the start of the notebook.

Looking at this function, it uses `plt.imshow(myarray)`

to convert our arrays to heatmaps. The issue here, is that we are not using the `origin`

argument. This causes imshow to plot our array in completely the wrong order: starting with the first row `likelihood_array[0,:] at the top, which as we can see from the image’s x axis *should* instead correspond to the likelihood array if x==8.

So how do we fix this? There’s a very simple bugfix: in the hidden cell, in the `plot_myarray()`

function, add `origin='lower'`

as an argument to imshow. This fixes the axes of all graphs in the notebook!:

Hope this clarifies some things for people who were confused by this.

PS: Note that all examples will remain ploted the wrong way around, as they have been prepared in advance. If your image is a mirror image of the example, you did it correctly!

PPS: I’ve also opened an issue on the github repo to address this issue