lognormal | Lognormal distribution | Build Tool library

There are three main pieces of advice I can give:

1. initial values matter and should not be so far off as to make portions of the model completely insensitive to the parameter values. Your initial model is sort of off by several orders of magnitude.
2. always look at the fit result. This is the primary result -- the plot of the fit is a representation of the actual numerical results. Not showing that you printed out the fit report is a good indication that you did not look at the actual result. Really, always look at the results.
3. if you are judging the quality of the fit based on a plot of the data and model, use how you choose to plot the data to guide how you fit the data. Specifically in your case, if you are plotting on a log scale, then fit the log of the data to the log of the model: fit in "log space".

Such a fit might look like this:

I think you're getting a bit confused between a bar chart and a histogram. You have a bar chart, which in your case is showing the change in one variable on the y axis against time on the x axis.

A histogram looks like a bar chart, but it shows the distribution of a single variable throughout its range. This has the value of the variable on the x axis, and the frequency with which that variable was found on the y axis. It makes sense to plot a density curve over a histogram, but not over a time series. If you are looking for a similar visual effect to the plot shown, the best you can get is probably to plot a moving average along with the bars, perhaps something like this:

Only agents that are connected to the engine in some way have access to the random number generator. And if your experiment is set to run main - like the example below - then all agents that want to use the random number generator must be connected to main in some way

So if you do this for example it wont work, and you get an NPE (Null Pointer Exception)

If you do this it will

Best option is to just create your own random number generator lognormal(0.1, 0.1, 5, new Random(0)); (Just put the random number generator somewhere so that you can use it again and again, else you will get the same number every time since it is the same (new) random object used to get the number)

This design is way better - see example here Why do two flowcharts set up exactly the same end with different results every time the simulation is run even when I use a fixed seed?

You have invalid range references. If the column letter is for example R:

• Col_Letter(StrtCol + CntH) & "89:92": evaluates to R89:92, but should be R89:R92.
• Col_Letter(StrtCol + CntH) & "93:93"): evaluates to R93:93, but should be R93.

This is easier to debug using some Range variables:

This is really not a scipy or a Python question, and is perhaps better suited for the sister site https://datascience.stackexchange.com/

However here is my proposed solution for coming up with good initial values. It does rely on the knowledge of the lognormal distribution as in for example https://en.wikipedia.org/wiki/Log-normal_distribution

The curve you are trying to fit is a shifted (by b) and scaled (by a) log-normal probability density. First we want to estimate these. As the log-normal density takes values in $[0,+infty)$, the initial estimate for parameter b coiuld be set to the lowest value of the intensity. Also, the integral of the log-normal density should be 1 for definition, hence a should be set to the integral of intensity shifted by b. After that we have a (true) lognormal density in intensity and we can estimate m and s from the mean of the distribution and its variance (in fact we just use the second moment). This logic is implemented in the function get_initial_values below. I also modify your func to make sure t is always strictly positive as otherwise it messes log, leads to division by zero, etc. Also it had a type n vs m. Here is the full code

You can use your function and apply it using pandas:

Scale the data first giving xx. Also we have shortened and fixed the code. (The input x is shown in the Note at the end. It is taken from the question but shown in reproducible form.)

Removing the macro logic and simplifying it slightly gets you this, which seems to work correctly to me. Note I simplified the N to ensure it runs quickly. Note that I'm not verify the logic, assuming that's correct on your side.

I'm really sorry that adding plotstyle = "ggplot need to be added to every line......But I think this may help you from that weird legend locations.