We need to talk about SVG bloat. For some reason, the standard advice for data visualization has become “just export it,” and it’s killing performance. I’ve seen dashboards where a single “smooth” chart was actually 250+ tiny line segments. On mobile, that’s a nightmare. The browser has to parse and render every single one of those coordinate points. If you want Orthogonal Distance Fitting (ODF), you’re looking for a way to stop being lazy with your path data.
Standard libraries like Matplotlib are great for research, but they are path-bloaters. They approximate curves using piecewise linear segments. It’s the safe, easy way out. But if you’re building high-performance dashboards, you need Orthogonal Distance Fitting to reduce those hundreds of segments into just a handful of cubic Bézier curves without losing visual accuracy.
The Math of Slimmer Paths
The secret sauce here isn’t just “smoothing.” It’s an optimization problem. The goal is to fit a Bézier curve that minimizes the orthogonal distance—the shortest distance from any point on the target function to the curve itself. While you’re at it, you should check out my thoughts on Modern CSS and SVG Favicons for more frontend asset tips.
The ODF algorithm typically follows this workflow:
- Chebyshev Approximation: Fit a Chebyshev series to the function first. Why? Because it provides rapid geometric convergence, which is far more efficient than standard cubic splines.
- Trust-Region Optimizer: Instead of simple gradient descent, a trust-region optimizer ensures we don’t get stuck in saddle points when adjusting the Bézier control points.
- Domain Splitting: If the maximum distance between the curve and the function exceeds your threshold, you split the domain and repeat.
Implementing Orthogonal Distance Fitting with Python
In the WordPress world, we usually handle the frontend, but if you’re processing data on the backend (perhaps via a Python-based microservice), the bbai library is the tool for the job. Here is how you take a raw Gaussian function and turn it into a compact path.
from bbai.graphics import BezierPath
import numpy as np
def f(t):
return np.exp(-t * t)
# Define our destination window
path = BezierPath(dst_xmin=0, dst_xmax=10, dst_ymin=0, dst_ymax=2)
# Apply Orthogonal Distance Fitting to fit the curve
path.fit(f, -2, 2)
# Output the compact TikZ/MetaPost code
print(path.tikz_)Instead of 200+ line segments, ODF gives you something like this:
\draw (0.000, 0.000)..controls (2.684, 0.092) and (3.273, 1.952)
.. (4.750, 2.000)..controls (6.229, 1.951) and (6.815, 0.092)
.. (9.500, 0.000);That is just two segments. The file size difference is massive when you scale this across complex plots like hypotrochoids or roses. If your Python services are still feeling sluggish after this, you might need to look into Py-Spy profiling to find the actual bottlenecks.
From TikZ to Clean SVG
Once you have the optimized path, you can use MetaPost to generate the final SVG. Most devs try to run LuaLaTeX with DVI conversion, but that’s overkill. MetaPost can output to SVG directly. Use the mpost utility found in any standard TeX Live installation. You can find more details in the MetaPost User Manual or check the official TikZ documentation.
Look, if this Orthogonal Distance Fitting stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress since the 4.x days, and I know how to optimize assets so they don’t choke your server.
The Takeaway
Stop accepting bloated vector graphics as the cost of doing business. Using Orthogonal Distance Fitting allows you to maintain mathematical precision while shipping ultra-compact SVGs. It’s better for the browser, better for your page speed scores, and better for your users on low-end devices. If you’re building a data-heavy dashboard, this isn’t optional—it’s a requirement for a professional build. Ship it.
