File size: 3,636 Bytes
f0ed0b4 | 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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | #!/usr/bin/env python3
"""Statistics over listings.csv, shared by the dataset build and the PDF.
Both `build.py` and `scripts/build_reference_pdf.py` state per-vendor internal
dimensions as fact. They must therefore compute them the same way, from one
definition — hence this module rather than a copy in each.
Pure functions over already-loaded rows; nothing here reads a path except
`load_listings`, and nothing touches the network.
"""
import csv
# Listings that are not straight-walled open-top boxes, and so must not feed the
# internal-dimension statistics. Matched on the notes build_listings.py writes.
EXCLUDE_FROM_INTERNAL_STATS = ("Nesting or folding crate", "Sold with a lid")
def load_listings(path):
if not path.exists():
return []
with path.open(encoding="utf-8") as f:
return list(csv.DictReader(f))
def median(xs):
s = sorted(xs)
mid = len(s) // 2
return s[mid] if len(s) % 2 else (s[mid - 1] + s[mid]) / 2
def footprint(row):
"""(type, L_mm, W_mm) for a listing, or None if unparseable."""
try:
return (row["type"], round(float(row["external_length_cm"]) * 10),
round(float(row["external_width_cm"]) * 10))
except ValueError:
return None
def internal_index(listings):
"""(type, L_mm, W_mm) -> {vendor: (median_int_L, median_int_W, median_height_deduction)}, mm.
One figure per *vendor*, not per listing. Salesbridges alone accounts for two
thirds of the 600x400 listings, mostly colour variants of the same mould; a
straight median over listings would just report Salesbridges' number and call
it a consensus. Taking each vendor's median first gives every catalogue one
vote, which is what the vendor capacity span means.
"""
per_vendor = {}
for r in listings:
key = footprint(r)
if (not key or not r["internal_length_cm"]
or any(x in r["notes"] for x in EXCLUDE_FROM_INTERNAL_STATS)):
continue
try:
il, iw = float(r["internal_length_cm"]) * 10, float(r["internal_width_cm"]) * 10
ded = float(r["external_height_cm"]) * 10 - float(r["internal_height_cm"]) * 10
except ValueError:
continue
per_vendor.setdefault(key, {}).setdefault(r["vendor"], []).append((il, iw, ded))
return {
key: {v: tuple(median([o[i] for o in obs]) for i in range(3))
for v, obs in vendors.items()}
for key, vendors in per_vendor.items()
}
def vendor_listing_counts(listings):
"""(type, L_mm, W_mm) -> {vendor: n listings publishing internal dimensions}."""
counts = {}
for r in listings:
key = footprint(r)
if (not key or not r["internal_length_cm"]
or any(x in r["notes"] for x in EXCLUDE_FROM_INTERNAL_STATS)):
continue
counts.setdefault(key, {})
counts[key][r["vendor"]] = counts[key].get(r["vendor"], 0) + 1
return counts
def capacity_span(index, kind, L, W, H):
"""(low, high) litres implied by the least and most generous vendor, or ("", "").
Same box, same external size, different published internal dimensions: at
600x400 the vendors range from 555x355 (Plastic Box Shop, measured near the
base) to 570x370 (Salesbridges, the top opening), which is an 11 % spread in
litres. Quoting one number without that range would be false precision.
"""
vendors = index.get((kind, L, W))
if not vendors:
return "", ""
caps = [il * iw * max(H - ded, 0) / 1e6 for il, iw, ded in vendors.values()]
return round(min(caps), 1), round(max(caps), 1)
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