Hi Pooya,

I'm sorry for the delayed response.

If you want to have a deterministic demand, you can simply provide identical realizations in the demand_sample. There is no need to hack the system by specifying a lognormal distribution with almost zero log_std. In the first pelicun LET example, we first generate demands, then extend the sample and feed it back to pelicun. If you prepare your own sample, you can skip the first part, and start at the point where we feed the sample back. Let me know if you need further details on this.

As for the standard deviation in the results, I am not sure that is a meaningful statistic in this case. Based on what I see in the first table, you only have zero damage or collapse (i.e., total loss) there. The mean is around 1 million, while the total loss is 21 million. About 10% of the results at 21 million might lead to a large standard deviation on paper, but your data really is at zero and at that large value, so there is not much dispersion. I am not sure those results are valid, though. When you fed in zero log_std, that might have led to unexpected behavior. I recommend trying again with the demand sample as I mentioned above and checking how the results come out.

Finally, I wanted to mention that you can remove the uncertainty from the fragility and consequence functions as well if that is what you need to do for your work. Let me know if you are interested in that I am happy to tell you more details.

Adam