Skip to content

README.md

Latest commit

 

History

History
62 lines (48 loc) · 2.36 KB

README.md

File metadata and controls

62 lines (48 loc) · 2.36 KB
Raw

pview example

A tiny, self-contained dataset for trying pview: 10 people, 9 images, and one person with no image so you can see the generated-card fallback.

Files

File What it is
people.csv 10 rows — name, age, department, city, start_date, salary, photo. The last row (Júlia Costa) has an empty photo.
images/ 9 images of random things, at deliberately mixed sizes/aspect ratios (wide 960×360, tall 300×820, large 1024×1024, small 240×240, …).
make_assets.py Regenerates images/ from scratch (no network, just Pillow).

Build it

From this directory:

pview build people.csv \
  --name-col name \
  --image-col photo \
  --card-fields name,department,city \
  --facet department=category --facet city=category \
  --title "pview demo — 10 people" \
  --out ./site

Expected output:

Built 10 items (1 generated, 0 image errors, 1 atlases) -> ./site

Add --single-file (and e.g. --out ./people.html) to get one portable HTML file with all assets inlined.

What it demonstrates

  • Scaling / fitting — every source image, whatever its dimensions, is scaled to fit a fixed square tile while keeping its aspect ratio, then centered. Open site/atlas/atlas_0.png to see them side by side: the wide horizon is letterboxed (full width, background bands top-and-bottom), the tall tower is pillarboxed (full height, background bands left-and-right), the squares fit edge-to-edge. The uncovered margins use the same per-item background color as the generated cards, so nothing is cropped away.
  • Generated cards — the one row with a blank photo (Júlia Costa) becomes a text card: her name plus the card-fields (department, city) drawn on a deterministic, per-item background color. The same fallback kicks in for any missing or unreadable image, so a bad path never breaks a build.
  • Facet inference — age/salary infer as numeric, start_date as date, and department/city are pinned to category via --facet.

The build emits the interactive viewer alongside the data: open site/index.html in a browser to filter, sort, search, and zoom the collection. The atlas PNG above is still handy for eyeballing how every tile was fit to its square at a glance.

Regenerate the images

python make_assets.py