Datasets:
File size: 2,016 Bytes
e1ced61 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | """Direct PlotQA observation comparisons, without digest validation.
These are the production drawing primitives. They are not an independent
renderer; independence applies to the two semantic executors only.
"""
from __future__ import annotations
import copy
import io
from pathlib import Path
_FONT_READY = False
def pixels(blob):
import numpy as np
from PIL import Image
return np.asarray(Image.open(io.BytesIO(blob)).convert("RGBA"))
def render_direct(world, manifest):
global _FONT_READY
import matplotlib
matplotlib.use("Agg")
from matplotlib import font_manager
from explicit_learning.renderers import plot
if not _FONT_READY:
root = Path(matplotlib.get_data_path()) / "fonts" / "ttf"
for name in ("DejaVuSans.ttf", "DejaVuSerif.ttf"):
font_manager.fontManager.addfont(root / name)
_FONT_READY = True
family = plot.PLOT_FAMILIES[manifest["renderer_id"]]
series = plot._parse_series(world)
domain = plot._build_domain(world, series)
canvas = plot._Canvas(manifest["width"], manifest["height"], family)
visible = plot._draw_plot(canvas, world, series, domain, family)
image, owners, counts = canvas.finish()
glyphs = [
{"node_id": g.node_id, "text": g.text, "bbox": [g.x0, g.y0, g.x1, g.y1]}
for g in sorted(canvas.glyphs, key=lambda x: (x.node_id, x.text, x.x0, x.y0))
]
return {
"image": image,
"owners": owners,
"glyphs": glyphs,
"node_table": ["__background__", *canvas.owner_ids],
"coverage": {n: counts.get(n, 0) for n in sorted(visible)},
"domain": domain.manifest_record(),
}
def visible_projection(world):
world = copy.deepcopy(world)
hidden = {n for n, flag in world.get("_node_visibility", {}).items() if flag == "hidden"}
for series in world.get("series", []):
for point in series.get("points", []):
if point["id"] in hidden:
point["y"] = None
return world
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