NoCodeBandit & BoneArray
NoCodeBandit
Hey BoneArray, I know you spend hours perfecting bone hierarchies—what if we built a quick no‑code workflow to auto‑name layers and auto‑weight bones so you can focus on the geometry instead of the messy naming?
BoneArray
Nice idea, but I won’t accept auto‑naming unless it follows my exact naming hierarchy. Auto‑weight is a joke unless it matches the precision of a hand‑painted curve. If the script can prove its math, I’ll consider it.
NoCodeBandit
Alright, here’s the deal: build a tiny node that spits out a table of bone weights for a few test vertices, then plot the curves on a graph. If the curve follows a cubic Bézier with the control points you pre‑define, you’ll see the math line up. Once that table looks legit, copy that logic into your automator, and you’ve got a script that’s as precise as a hand‑painted curve and as snappy as a punch‑line. Try it and let me know if the numbers look good or if you need more math gymnastics.
BoneArray
Sure, I’ll spit out a table of vertex weights and plot the curves. If the points line up with my cubic Bézier definition, the math will look solid. Once the graph passes my scrutiny, you can copy the logic into your automator. If any of the numbers look off, I’ll point out where the interpolation fails. No unnecessary constraints, just pure precision.
NoCodeBandit
Sounds like a solid plan. Just dump a quick Python snippet that spits a CSV of bone, vertex, weight, then feed it into Excel or a quick Matplotlib graph. Once you see the curve follow your Bézier control points, copy the weight‑calc function into the automator. If anything looks off, let me know which part of the interpolation is skewing—maybe it’s a hidden edge case. Happy hacking.
BoneArray
Here’s a bare‑bones snippet.
```python
import csv
import math
import matplotlib.pyplot as plt
# test data
vertices = range(10) # 10 test vertices
bones = ['bone1', 'bone2'] # two bones
# Bézier control points (t, weight) for bone1
ctrl = [(0,0.0), (0.3,0.7), (0.7,0.3), (1,0.0)]
def bezier(t, pts):
# cubic Bézier
return (
(1-t)**3 * pts[0][1] +
3*(1-t)**2 * t * pts[1][1] +
3*(1-t) * t**2 * pts[2][1] +
t**3 * pts[3][1]
)
# generate CSV
with open('weights.csv','w',newline='') as f:
w = csv.writer(f)
w.writerow(['vertex','bone','weight'])
for v in vertices:
t = v / (len(vertices)-1)
w1 = bezier(t, ctrl)
w2 = 1 - w1
w.writerow([v,'bone1',w1])
w.writerow([v,'bone2',w2])
# plot
t_vals = [v/(len(vertices)-1) for v in vertices]
w1_vals = [bezier(t,ctrl) for t in t_vals]
w2_vals = [1 - w for w in w1_vals]
plt.plot(t_vals,w1_vals,'b-',label='bone1')
plt.plot(t_vals,w2_vals,'r-',label='bone2')
plt.legend()
plt.show()
```
Run it, check the CSV and the plot. If the curves deviate, the problem is either in the Bézier calculation or the weight normalization. The function `bezier` is what you’ll copy into the automator once it looks good.
NoCodeBandit
Looks solid—give it a spin and watch that blue curve swoop right between your control points. If the plot matches the CSV numbers, you’re good to lock that `bezier` function into the automator. If anything glitches, hit me up with the off‑point and we’ll tweak the math. Happy plotting!