Hi all,

I'm working on a Python tool to help me create procedural textures for Poser. The idea is that I put a formula that depends on the UV position in a script, and the tool shows me a preview and writes out an .mt5 file that, when loaded into Poser, creates a compound node. A mini-matmatic, if you will.

Now for the preview to be correct, I need to understand what the relevant material room nodes do, and the one I'm focusing on for now is the Math_Functions node. The documentation of the various available functions in Math Nodes is not always very clear, and sometimes incorrect. For example, it says that for "log" the second input value is ignored, but in fact it is the base of the logarithm.

So, I'm wondering if there might be some other source of information on these functions with a bit more detail. It's not super-important because for most of them I can figure out how they work, but it would make things a tad easier.

For reference, here's what I have so far (in Python, after import numpy as _np). Not super-confident about those last three, so I might dig for my texture baking kit for Poser and do a numerical comparison on top of the visual one. There may also be some edge cases I got wrong.

I found two references for bias and gain on the interwebs that used different bias functions (Poser seems to go with the original one, not the faster alternative) and messed up the definition of the gain in two unique ways. I'm somewhat tempted to read the original paper, but I'm also way too lazy...

I never found any Poser docs for the math behind any of the nodes. I reverse engineered everything.

Your bias is correct. You can actually verify this by hand, building a ** (log(b) / log(.5)) in nodes and then take THAT node, subtract with an actual math:bias node having the same inputs, then take the absolute value of that difference and multiply by 1000 and plug that into ambient_color. If there's any deviation it will show up as not black. Here I used U and V as my two inputs, and the lower four nodes do the explicit math. The upper two compare and amplify differences from the real Bias function.

I never saw the 2nd input to log do anything. I'm in Poser 12 and lower. What did you observe?

One of the craziest things I ever noticed was when studying the bias(U, V), the pattern is completely symmetrical about the diagonal. I looked more deeply at the math and found you can swap the inputs and get the same results:

a ** (log(b) / log(.5))

(e ** log(a)) ** (log(b) / log(.5))

e ** (log(a) * log(b) / log(.5))

e ** (log(b) * log(a) / log(.5))

(e ** log(b)) ** (log(a) / log(.5))

b ** (log(a) / log(.5))

All of these expressions have the same value.

I used "log" above but I really meant "ln". Depends on whether you're an engineer or scientist or mathematician. 

I never found any Poser docs for the math behind any of the nodes. I reverse engineered everything.

Your bias is correct. You can actually verify this by hand, building a ** (log(b) / log(.5)) in nodes and then take THAT node, subtract with an actual math:bias node having the same inputs, then take the absolute value of that difference and multiply by 1000 and plug that into ambient_color. If there's any deviation it will show up as not black. Here I used U and V as my two inputs, and the lower four nodes do the explicit math. The upper two compare and amplify differences from the real Bias function.

Excellent, thanks! I was considering something along those lines but somehow the multiplication at the end didn't occur to me. Brainworms, I swear.

bagginsbill posted at 2:12 PM Mon, 21 October 2024 - #4490616

I never saw the 2nd input to log do anything. I'm in Poser 12 and lower. What did you observe?

One of the craziest things I ever noticed was when studying the bias(U, V), the pattern is completely symmetrical about the diagonal. I looked more deeply at the math and found you can swap the inputs and get the same results:

Neat!

Probably not worth a separate post, but I got my little tool working. This Python code:

produces this compound node (after loading from file):

which can be used like this (for example):

If I add this to the Python script:

I get shown a preview image. This is implemented very hackily at the moment, but easy to fix.

Now this also means I get to write formulas for testing my assumptions about how the Poser math functions work, rather than having to wire the test networks up by hand.

If you are doing a lot of work in the material room this freebie may be useful as you can quickly check the values coming out of nodes.  That way you get a numeric reading of the output instead of just a color.

Value Viewer for Poser Super Fly

Thanks, very neat!