Saving and Exporting¶

Once you have a CP you like, you'll want to send it somewhere — a plotter, a folder simulator, or a 3D printer. This notebook covers the export formats pleat ships with:

• .heg — pleat's native YAML serialization.
• SVG — vector for laser cutters / pen plotters.
• A high-level overlap.save_results that writes a whole result directory in one call.

STL files can be generated via marching cubes, e.g. for 3D printers. It requires the [threed] install extra.

The FOLD format is currently not supported, but would be nice to have in the future.

In [1]:

import matplotlib
matplotlib.rcParams['figure.figsize'] = (5, 5)
from pleat import (
    example_graphs,
    example_tilesets,
    rendering,
)
from pleat.rendering import multi_show

import matplotlib matplotlib.rcParams['figure.figsize'] = (5, 5) from pleat import ( example_graphs, example_tilesets, rendering, ) from pleat.rendering import multi_show

In [2]:

G = example_graphs.from_tiles(example_tilesets.platonic(4), rings=2)
G.recompute_lengths_and_angles()

G = example_graphs.from_tiles(example_tilesets.platonic(4), rings=2) G.recompute_lengths_and_angles()

.heg save¶

Pleat's native YAML serialization captures the full graph structure plus arbitrary attributes.

Note: the load path currently uses yaml.SafeLoader, so graphs with non-trivial Python attributes (tuples, numpy scalars) save but don't round-trip.

In [3]:

import tempfile, os
from pleat import io

with tempfile.TemporaryDirectory() as d:
    path = os.path.join(d, 'pattern.heg')
    io.save_graph(path, G)
    size = os.path.getsize(path)
    print(f'wrote {size} bytes')

    # load it back
    G2 = io.load_graph(path)

multi_show(
    [G, G2],
    titles=['original', 'loaded'],
)

import tempfile, os from pleat import io with tempfile.TemporaryDirectory() as d: path = os.path.join(d, 'pattern.heg') io.save_graph(path, G) size = os.path.getsize(path) print(f'wrote {size} bytes') # load it back G2 = io.load_graph(path) multi_show( [G, G2], titles=['original', 'loaded'], )

wrote 23055 bytes

SVG export via G.save(...)¶

G.save('out') writes both out.svg and out.png. G.show() displays inline (vector SVG in Jupyter) without writing files. The SVG is the same vector drawing that CairoRenderer produced — open it in a browser or Inkscape for the full quality.

In [4]:

import tempfile, os
with tempfile.TemporaryDirectory() as d:
    out = os.path.join(d, 'pattern')
    G.save(out, **rendering.CREASE_PATTERN_PRESET)
    print('files in temp dir:', sorted(os.listdir(d)))
    print('SVG head:')
    print(open(out + '.svg').read()[:200])

import tempfile, os with tempfile.TemporaryDirectory() as d: out = os.path.join(d, 'pattern') G.save(out, **rendering.CREASE_PATTERN_PRESET) print('files in temp dir:', sorted(os.listdir(d))) print('SVG head:') print(open(out + '.svg').read()[:200])

files in temp dir: ['pattern.png', 'pattern.svg']
SVG head:
<?xml version="1.0" encoding="UTF-8"?>
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="512" height="512" viewBox="0 0 512 512">
<rect x="-51.2" y="-51.2" widt

Plotter-ready SVG via SvgwriteRenderer¶

For laser-cutter / pen-plotter pipelines the dedicated SvgwriteRenderer produces an SVG split into {name}_borders.svg / {name}_interior.svg (so you can use different tool heads for cut vs. score).

All-in-one with overlap.save_results¶

If you've gone through fold_complete (demonstrated in the Shrink-Rotate notebook), save_results(result, path) writes the CP, both folded views, a back-lit composite, and a plotter-ready SVG in one call.