I’m not sure if this is worth reporting as a very minor error, but I recently fit a rather straightforward model (1 variable sampled, <500k data points, sampling done in under 10 minutes, with no warnings reported at all), and `pm.traceplot(my_difficult_trace)`

simply does not work for its trace. It ends up taking up all the RAM in my system and crashing my kernel, as well as lagging my computer to a complete standstill, all within a few minutes.

The first issue is that the trace may be buggy in some sense, but I reran the model with no errors, and the new trace displayed the same behavior, not allowing `pm.traceplot()`

to work. The second issue is how problematic it is that a difficult plot would be enough to freeze my computer within a few minutes if it was proving the slightest bit difficult, but I guess that’s a matplotlib error

Would someone examine the trace I am uploading, and see if they experience a similar error? Thank you all for your attention on this matter.

Link to the trace (mega seemed the most efficient way to share it) is here.

Code:

```
with pm.Model() as model:
# Prior
# The upper bound is the range of my dataset, multiplied by 5 to give a naive but simple diffuse prior
sigma = pm.Uniform('sigma',
lower=0,
upper = 5 * (max(np.hstack(my_data[3])) - min(np.hstack(my_data[3]))) )
# Likelihood
estimate = pm.Normal('estimate', mu = np.mean( np.hstack(my_data[3]) ), sigma = sigma,
observed = np.hstack(my_data[3]))
trace = pm.sample(cores=4, n_init=20000, draws=2000, tune=8000)
```