spa-l2-cairo-shop-knn
Map (reference ← swipe → agent)
0:00
I'm putting together a retail-density readout for a downtown Cairo brief. `cairo_retail` has two layers, `shops` and `anchors`. For each anchor I need the 5 nearest shops with distance in metres and a within-1 km flag, plus a 5x3 distance matrix from those 5 shops to the anchor's 3 closest sibling anchors. The chain names in the source data have inconsistent spellings, so the output should use consistent names: the same `shop_id` always carries the same `normalised_name`, and all shops belonging to one chain share a single canonical spelling. Tidy the anchor names too.
Please write `market_neighbourhoods.json` as a top-level JSON array with one record per anchor. Each record carries the keys `anchor_id`, `anchor_name_normalised` (non-empty string), `knn` (exactly 5 entries sorted by ascending `distance_m`), and `full_distance_matrix_m` (5 rows by 3 numeric columns; rows follow the knn order, columns correspond to the sibling anchors in distance-ascending order). Each `knn` entry has `shop_id`, `normalised_name`, `distance_m` (finite numeric, in metres), and `within_1km` (boolean, true when `distance_m` is at most 1000).
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by understanding the data. I need to read the `cairo_retail` file to understand its structure first. Let me check what format it's in.
0:03
Bash
ls /work/
cairo_retail.gpkg
0:06
Let me first explore the structure of the GeoPackage file to understand what layers and columns are available.
0:06
(no output)
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gpd.list_layers('/work/cairo_retail.gpkg')
"
0:10
Let me explore the file more thoroughly.
0:10
Bash
python3 -c "
python3 -c "
import fiona
print(fiona.list_layers('/work/cairo_retail.gpkg'))
"
Traceback (most recent call last):
File "<string>", line 3, in <module>
print(fiona.list_layers('/work/cairo_retail.gpkg'))
^^^^^^^^^^^^^^^^^
AttributeError: module 'fiona' has no attribute 'list_layers'. Did you mean: 'listlayers'?
0:13
Let me try again with the correct function name.
0:13
Bash
python3 -c "
python3 -c "
import geopandas as gpd
# Try reading layers
for lyr in gpd.list_layers('/work/cairo_retail.gpkg')['name']:
print(lyr)
"
shops anchors
0:19
Good, there are two layers: `shops` and `anchors`. Let me explore the schema and data.
0:19
Bash
python3 -c "
python3 -c "
import geopandas as gpd
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
print('=== SHOPS ===')
print('columns:', list(shops.columns))
print('shape:', shops.shape)
print(shops.head(10).to_string())
print()
print('dtypes:', shops.dtypes)
print()
anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')
print('=== ANCHORS ===')
print('columns:', list(anchors.columns))
print('shape:', anchors.shape)
print(anchors.head(10).to_string())
print()
print('dtypes:', anchors.dtypes)
"
=== SHOPS === columns: ['shop_id', 'raw_name', 'geometry'] shape: (10000, 3) shop_id raw_name geometry 0 S00001 OSCAR POINT (637478.058 814957.398) 1 S00002 Local Shop 00002 POINT (651773.775 816149.979) 2 S00003 Local Shop 00003 POINT (640341.689 815148.396) 3 S00004 Local Shop 00004 POINT (640225.465 805311.132) 4 S00005 Local Shop 00005 POINT (647199.272 818195.959) 5 S00006 مترو POINT (647363.601 817078.397) 6 S00007 Local Shop 00007 POINT (637452.604 813452.721) 7 S00008 Carrefour POINT (642884.749 820068.564) 8 S00009 Local Shop 00009 POINT (638401.854 815382.513) 9 S00010 Carrefour POINT (645066.08 808334.791) dtypes: shop_id str raw_name str geometry geometry dtype: object === ANCHORS === columns: ['anchor_id', 'anchor_name', 'geometry'] shape: (100, 3) anchor_id anchor_name geometry 0 M001 TAHRIR SQUARE PLAZA POINT (635109.343 805565.909) 1 M002 Zamalek Riverside POINT (637039.843 805569.597) 2 M003 Maadi Corniche POINT (638970.345 805573.62) 3 M004 HELIOPOLIS SQUARE POINT (640900.848 805577.981) 4 M005 New Cairo Tagamoa POINT (642831.353 805582.677) 5 M006 Nasr City Hub POINT (644761.86 805587.711) 6 M007 MOHANDESSIN CENTRE POINT (646692.368 805593.081) 7 M008 Garden City Walk POINT (648622.879 805598.787) 8 M009 Dokki Market POINT (650553.391 805604.83) 9 M010 GIZA PYRAMIDS GATE POINT (652483.906 805611.21) dtypes: anchor_id str anchor_name str geometry geometry dtype: object
0:25
Let me understand the data: 1. **shops** layer: 10,000 shops with columns `shop_id`, `raw_name`, and `geometry` (projected CRS, looks like metric). 2. **anchors** layer: 100 anchors with `anchor_id`, `anchor_name`, and `geometry`. The CRS appears to be a projected coordinate system (the coordinates are in the hundreds of thousands range), so `distance_m` would be in metres already. Let me first check the CRS and explore the shop names to understand the normalisation needed. Key requirements: 1. **Normalised names**: Inconsistent spellings need to be unified. Same `shop_id` always carries the same `normalised_name`. All shops belonging to one chain share a single canonical spelling. 2. For each anchor: find the 5 nearest shops with distance in metres and a within_1km flag. 3. For each anchor: a 5x3 distance matrix from those 5 shops to the anchor's 3 closest sibling anchors. 4. The output is `market_neighbourhoods.json` - a top-level JSON array. Let me explore the raw names more deeply to figure out the normalisation logic.
0:25
Bash
python3 -c "
python3 -c "
import geopandas as gpd
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
# CRS
print('CRS:', shops.crs)
print()
# Unique raw_names
names = shops['raw_name'].value_counts()
print('Unique shop raw_name values:')
print(names.head(50))
print('...')
print()
print('Total unique names:', len(names))
"
CRS: EPSG:22992 Unique shop raw_name values: raw_name هايبر وان 183 OSCAR 176 Seoudi 176 spinneys cairo 174 Khair Zaman 171 Carrefour Egypt 169 kheir zaman 168 metro 165 Metro Markets 164 Oscar Grand Stores 163 خير زمان 162 Seoudi Market 160 Hyper One 158 Kheir Zaman 158 مترو 156 سبينيز 156 abou zekry 156 hyperone 155 اوسكار 154 Metro Market 153 HyperOne 152 Spinneys 150 كارفور 150 Oscar 150 سعودي 146 Spineys 145 Carrefour 143 seoudi supermarket 139 Abu Zikri 137 carrefour 136 أبو زكري 136 Abu Zekry 136 Local Shop 00002 1 Local Shop 00003 1 Local Shop 00004 1 Local Shop 00005 1 Local Shop 00007 1 Local Shop 00009 1 Local Shop 00011 1 Local Shop 00013 1 Local Shop 00014 1 Local Shop 00015 1 Local Shop 00017 1 Local Shop 00018 1 Local Shop 00019 1 Local Shop 00020 1 Local Shop 00023 1 Local Shop 00024 1 Local Shop 00025 1 Local Shop 00027 1 Name: count, dtype: int64 ... Total unique names: 5035
0:30
Let me explore the data more. I see that the CRS is EPSG:22992 (a metric CRS - likely Egypt zone). Let me understand the name variations better. I need to: 1. Normalise shop names - many variations of the same chains (Carrefour, spinneys, metro, etc.) 2. Normalise anchor names too 3. For each anchor, find 5 nearest shops with distances 4. Build a 5x3 distance matrix from those 5 shops to the anchor's 3 closest sibling anchors Let me look at the anchor names and understand what "sibling anchors" means - anchors with the same normalised name I think. Let me first explore more data.
0:30
Bash
python3 -c "
python3 -c "
import geopandas as gpd
anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')
print('Unique anchor_name values:')
names = anchors['anchor_name'].value_counts()
for n, c in names.items():
print(f' {repr(n):40s} count={c}')
print()
print('Total anchors:', len(anchors))
"
Unique anchor_name values: 'TAHRIR SQUARE PLAZA' count=1 'Zamalek Riverside' count=1 ' Maadi Corniche ' count=1 'HELIOPOLIS SQUARE' count=1 'New Cairo Tagamoa' count=1 ' Nasr City Hub ' count=1 'MOHANDESSIN CENTRE' count=1 'Garden City Walk' count=1 ' Dokki Market ' count=1 'GIZA PYRAMIDS GATE' count=1 'Roxy Square' count=1 ' Ramses Crossing ' count=1 'SAYEDA ZEINAB PLAZA' count=1 'Khan El Khalili Approach' count=1 ' Bab Al Louq Corner ' count=1 'KORBA QUARTER' count=1 'Manial Riverbank' count=1 ' Shubra North ' count=1 'AIN SHAMS PLAZA' count=1 'Abbasiya Junction' count=1 ' Boulaq Edge ' count=1 'GARBIYA PLAZA' count=1 'Sakakini Approach' count=1 ' Dar El Salaam ' count=1 'EL MARG HUB' count=1 'Helwan Centre' count=1 ' Maasara Crossing ' count=1 'TORA EDGE' count=1 'Mokattam Heights' count=1 ' Nozha Promenade ' count=1 'SHERATON HELIOPOLIS' count=1 'Triumph Square' count=1 ' Cleopatra Plaza ' count=1 'SALAH SALEM STRIP' count=1 'Autostrad Corner' count=1 ' El Rehab Gate One ' count=1 'EL REHAB GATE TWO' count=1 'Madinaty Promenade' count=1 ' Fifth Settlement North ' count=1 'FIFTH SETTLEMENT SOUTH' count=1 'American University Gate' count=1 ' Police Academy Strip ' count=1 'RING ROAD NORTH' count=1 'Ring Road East' count=1 ' Ring Road West ' count=1 'CITY STARS MALL' count=1 'Cairo Festival City' count=1 ' Mall of Egypt Gate ' count=1 'TAGAMOA FIRST' count=1 'Tagamoa Third' count=1 ' El Mokattam Plateau ' count=1 'AL AHLY STADIUM' count=1 'Cairo Stadium' count=1 ' Sharkawi Plaza ' count=1 'EL OBOUR HUB' count=1 'Shoubra Mazallat' count=1 ' Abdeen Palace Edge ' count=1 'EL HUSSEIN SQUARE' count=1 'Al Ghouriya Strip' count=1 ' El Mosky Quarter ' count=1 'BAB ZUWEILA APPROACH' count=1 'Ataba Square' count=1 ' Opera Square ' count=1 'TALAAT HARB PLAZA' count=1 'Soliman Pasha Corner' count=1 ' Sherif Street ' count=1 'QASR EL NILE' count=1 'Kasr El Aini Strip' count=1 ' El Sayeda Aisha ' count=1 'KOBRI EL QUBBA' count=1 'Mar Mina Plaza' count=1 ' Saint Fatima Hub ' count=1 'EL NOZHA EL GEDIDA' count=1 'Rabaa Square' count=1 ' Tagamoa El Saba ' count=1 'BAHTEEM CROSSING' count=1 'El Salam City' count=1 ' Madinet Nasr Eighth Zone ' count=1 'MADINET NASR TENTH ZONE' count=1 'El Hadaba El Wosta' count=1 ' Mokattam Sector One ' count=1 'MOKATTAM SECTOR SIX' count=1 'El Maadi Degla' count=1 ' Maadi Sarayat ' count=1 'MAADI CORNISH' count=1 'Old Cairo Babylon' count=1 ' Coptic Cairo Plaza ' count=1 'FUSTAT PARK EDGE' count=1 'Manial Bridge' count=1 ' Embaba Crossing ' count=1 'IMBABA AIRPORT STRIP' count=1 'Mit Okba Plaza' count=1 ' El Agouza Riverside ' count=1 'EL SAHEL JUNCTION' count=1 'Rod El Farag Bridge' count=1 ' Shubra El Kheima Centre ' count=1 'EL SAWAH CORNER' count=1 'Demerdash Plaza' count=1 ' El Demerdash Hospital Edge ' count=1 'RAMSES HILTON PLAZA' count=1 Total anchors: 100
0:48
Let me analyze the data: 1. **Shops (10000)**: CRS is EPSG:22992 (a metric CRS for Egypt). Need to normalise shop names. 2. **Anchors (100)**: Need to strip whitespace and normalise names. For shop name normalisation, I need to group the inconsistent spellings into canonical names: - "هايبر وان", "Hyper One", "hyperone", "HyperOne", "HyperOne" → maybe "Hyper One" - "OSCAR", "Oscar Grand Stores", "اوسكار", "Oscar" → "Oscar" - "Seoudi", "Seoudi Market", "سعودي", "seoudi supermarket" → "Seoudi" - "spinneys cairo", "Spinneys", "Spineys", "سبينيز" → "Spinneys" - "Khair Zaman", "kheir zaman", "Kheir Zaman", "خير زمان" → "Kheir Zaman" - "Carrefour Egypt", "كارفور", "Carrefour", "carrefour" → "Carrefour" - "metro", "Metro Markets", "Metro Market", "مترو" → "Metro" - "abou zekry", "Abu Zikri", "أبو زكري", "Abu Zekry" → "Abu Zekry" - "Local Shop XXXXX" → keep as is (each is unique single shop) For anchor name normalisation: strip whitespace, and probably title-case them consistently? Let me think about what "tidy" means. The data has leading/trailing spaces. Let me strip and normalize to a clean format. Let me write the full script now. Implementation plan: 1. Read both layers from GeoPackage 2. Define a mapping for shop name normalisation 3. Apply normalised names to shops 4. For each anchor: a. Calculate distances to all shops (in metres) b. Find 5 nearest shops c. For each of those 5 shops, determine if within 1km d. Calculate distances from those 5 shops to the anchor's 3 closest sibling anchors e. Build the output record For the distance matrix from the 5 shops to the 3 closest sibling anchors: - "sibling anchors" = all other anchors (since they're all the same type - anchors) - For each anchor, find its 3 closest other anchors - Then compute a 5x3 matrix: rows are the 5 nearest shops (in knn order), columns are the 3 closest sibling anchors (in distance-ascending order) Wait, "sibling anchors" - does this mean all other anchors? Or just anchors that are in the same chain? Looking at the data, each anchor has a unique name and there's no chain info. So "sibling anchors" probably means all other anchors. Let me write the script. For distance calculation, since CRS is EPSG:22992 (metric), I can use the geometry coordinates directly to compute Euclidean distances as they're already in metres. Let me be careful about the name normalisation: Shop names to normalise: - "هايبر وان" → "Hyper One" - "Hyper One" → "Hyper One" - "hyperone" → "Hyper One" - "HyperOne" → "Hyper One" - "OSCAR" → "Oscar" - "Oscar Grand Stores" → "Oscar" - "اوسكار" → "Oscar" - "Oscar" → "Oscar" - "Seoudi" → "Seoudi" - "Seoudi Market" → "Seoudi" - "سعودي" → "Seoudi" - "seoudi supermarket" → "Seoudi" - "spinneys cairo" → "Spinneys" (or "Spinneys Cairo"? Let me think... since other entries are just "Spinneys" and "Spineys" (typo), "spinneys cairo" should map to "Spinneys") - "Spinneys" → "Spinneys" - "Spineys" → "Spinneys" - "سبينيز" → "Spinneys" - "Khair Zaman" → "Kheir Zaman" - "kheir zaman" → "Kheir Zaman" - "Kheir Zaman" → "Kheir Zaman" - "خير زمان" → "Kheir Zaman" - "Carrefour Egypt" → "Carrefour" - "كارفور" → "Carrefour" - "Carrefour" → "Carrefour" - "carrefour" → "Carrefour" - "metro" → "Metro" - "Metro Markets" → "Metro" - "Metro Market" → "Metro" - "مترو" → "Metro" - "abou zekry" → "Abu Zekry" - "Abu Zikri" → "Abu Zekry" - "أبو زكري" → "Abu Zekry" - "Abu Zekry" → "Abu Zekry" - "Local Shop XXXXX" → keep as is For anchor names: just strip whitespace and maybe make them title case? But names like "TAHRIR SQUARE PLAZA" - should I keep it as is after stripping? Let me think... "tidy" probably means: - Strip leading/trailing whitespace - Maybe normalize case consistently? The mixed case (some ALL CAPS, some Title Case, some lowercase) needs tidying. I think a reasonable approach: strip whitespace, then title case each word. But for acronyms or obvious proper names... Let me just strip whitespace and apply `.strip().title()`. Let me check: "TAHRIR SQUARE PLAZA" → strip → "TAHRIR SQUARE PLAZA" → title → "Tahrir Square Plaza" " Maadi Corniche " → strip → "Maadi Corniche" → title → "Maadi Corniche" " Nasr City Hub " → strip → "Nasr City Hub" → title → "Nasr City Hub" That seems reasonable. Now let me write the script. For efficiency with 10000 shops and 100 anchors, computing all-pair distances (100 × 10000 = 1M distances) is fine in pandas/numpy. Let me implement this. Actually, for the "within_1km" flag: true when distance_m <= 1000. Let me carefully build this now. For the full_distance_matrix_m: 5 rows × 3 columns. - Rows follow the knn order (5 nearest shops to this anchor) - Columns correspond to the 3 closest sibling anchors (other anchors) in distance-ascending order from this anchor So for each anchor A: 1. Find the 5 nearest shops (already done for knn) 2. Find the 3 closest other anchors (siblings) 3. For each of those 5 shops, compute distance to each of those 3 sibling anchors 4. That gives the 5×3 matrix Let me code it up.
0:48
Bash
python3 -c "
python3 -c "
import geopandas as gpd
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
# Check distribution of chain names more carefully
print('=== All chain-like names (non Local Shop) ===')
chains = shops[~shops['raw_name'].str.startswith('Local Shop')]
print(chains['raw_name'].value_counts())
print()
print('Total shops:', len(shops))
print('Non-local shops:', len(chains))
print('Local shops:', len(shops) - len(chains))
"
=== All chain-like names (non Local Shop) === raw_name هايبر وان 183 OSCAR 176 Seoudi 176 spinneys cairo 174 Khair Zaman 171 Carrefour Egypt 169 kheir zaman 168 metro 165 Metro Markets 164 Oscar Grand Stores 163 خير زمان 162 Seoudi Market 160 Hyper One 158 Kheir Zaman 158 مترو 156 سبينيز 156 abou zekry 156 hyperone 155 اوسكار 154 Metro Market 153 HyperOne 152 Spinneys 150 كارفور 150 Oscar 150 سعودي 146 Spineys 145 Carrefour 143 seoudi supermarket 139 Abu Zikri 137 carrefour 136 أبو زكري 136 Abu Zekry 136 Name: count, dtype: int64 Total shops: 10000 Non-local shops: 4997 Local shops: 5003
1:08
Let me analyze the chain names and map them to normalized names. I see these chains: 1. **OSCAR** variants: OSCAR, Oscar Grand Stores, اوسكار, Oscar (maybe others) → "Oscar" 2. **Carrefour** variants: Carrefour Egypt, كارفور, Carrefour, carrefour → "Carrefour" 3. **Metro** variants: metro, Metro Markets, مترو, Metro Market → "Metro" 4. **Hyper One** variants: هايبر وان, Hyper One, hyperone, HyperOne → "Hyper One" 5. **Spinneys** variants: spinneys cairo, سبينيز, Spinneys, Spineys → "Spinneys" 6. **Seoudi** variants: Seoudi, Seoudi Market, سعودي, seoudi supermarket → "Seoudi" 7. **Kheir Zaman** variants: Khair Zaman, kheir zaman, خير زمان, Kheir Zaman → "Kheir Zaman" 8. **Abu Zekry** variants: abou zekry, Abu Zikri, أبو زكري, Abu Zekry → "Abu Zekry" So each shop_id gets a normalized_name based on its raw_name. Let me build a mapping dictionary. For anchors, the names have inconsistent whitespace (leading/trailing spaces). I need to strip them and then do a title-case normalization. Let me look at them more carefully: Some are ALL CAPS, some are Mixed Case, some have extra spaces. - Strip whitespace - I'll normalize to Title Case (capitalize each word) Let me write the full solution.
1:08
1:08
Write
/work/solve.py
content · 152 lines · py
import geopandas as gpd
import numpy as np
import json
import warnings
warnings.filterwarnings("ignore")
# ── 1. Load data ──────────────────────────────────────────────────────────────
shops = gpd.read_file("/work/cairo_retail.gpkg", layer="shops")
anchors = gpd.read_file("/work/cairo_retail.gpkg", layer="anchors")
# Both are in EPSG:22992 (metres) — perfect for distance calculation.
assert shops.crs == anchors.crs
# ── 2. Normalise shop names ───────────────────────────────────────────────────
# Build a mapping from every raw_name to a single canonical chain name.
name_map = {
# Oscar
"OSCAR": "Oscar",
"Oscar Grand Stores": "Oscar",
"اوسكار": "Oscar",
"Oscar": "Oscar",
# Carrefour
"Carrefour Egypt": "Carrefour",
"كارفور": "Carrefour",
"Carrefour": "Carrefour",
"carrefour": "Carrefour",
# Metro
"metro": "Metro",
"Metro Markets": "Metro",
"مترو": "Metro",
"Metro Market": "Metro",
# Hyper One
"هايبر وان": "Hyper One",
"Hyper One": "Hyper One",
"hyperone": "Hyper One",
"HyperOne": "Hyper One",
# Spinneys
"spinneys cairo": "Spinneys",
"سبينيز": "Spinneys",
"Spinneys": "Spinneys",
"Spineys": "Spinneys",
# Seoudi
"Seoudi": "Seoudi",
"Seoudi Market": "Seoudi",
"سعودي": "Seoudi",
"seoudi supermarket": "Seoudi",
# Kheir Zaman
"Khair Zaman": "Kheir Zaman",
"kheir zaman": "Kheir Zaman",
"خير زمان": "Kheir Zaman",
"Kheir Zaman": "Kheir Zaman",
# Abu Zekry
"abou zekry": "Abu Zekry",
"Abu Zikri": "Abu Zekry",
"أبو زكري": "Abu Zekry",
"Abu Zekry": "Abu Zekry",
}
shops["normalised_name"] = shops["raw_name"].map(name_map).fillna(shops["raw_name"])
# ── 3. Normalise anchor names ─────────────────────────────────────────────────
# Strip whitespace and convert to Title Case
anchors["anchor_name_normalised"] = (
anchors["anchor_name"]
.str.strip()
.str.title()
)
# ── 4. KNN: for each anchor, find nearest 5 shops ─────────────────────────────
# We'll use scipy's cKDTree for speed (100 anchors, 10000 shops).
from scipy.spatial import cKDTree
shop_coords = np.array([(p.x, p.y) for p in shops.geometry])
anchor_coords = np.array([(p.x, p.y) for p in anchors.geometry])
tree = cKDTree(shop_coords)
# Query 5 nearest neighbours (k=6 because first is self if coincident, but shops
# and anchors are different points so k=5 is fine — but let's use k=6 and skip
# index 0 just to be safe, although they're distinct geometries)
k = 5
distances, indices = tree.query(anchor_coords, k=k)
# If k=5 always returns 5 results, fine. Handle edge case: if <5 shops exist
# (impossible here, but be safe).
if k > len(shops):
k = len(shops)
distances, indices = tree.query(anchor_coords, k=k)
# ── 5. Sibling anchors: for each anchor, find 3 closest other anchors ─────────
# Build a cKDTree on anchors themselves
anchor_tree = cKDTree(anchor_coords)
# Query k=4 (nearest 4 including self)
sib_distances, sib_indices = anchor_tree.query(anchor_coords, k=min(4, len(anchors)))
# ── 6. Build output records ───────────────────────────────────────────────────
results = []
for a_idx in range(len(anchors)):
anchor_id = anchors.iloc[a_idx]["anchor_id"]
anchor_name_norm = anchors.iloc[a_idx]["anchor_name_normalised"]
# --- KNN entries ---
knn_entries = []
knn_shop_indices = [] # keep track for the matrix rows
for rank in range(k):
s_idx = indices[a_idx][rank]
d = float(distances[a_idx][rank])
knn_shop_indices.append(s_idx)
knn_entries.append({
"shop_id": shops.iloc[s_idx]["shop_id"],
"normalised_name": shops.iloc[s_idx]["normalised_name"],
"distance_m": round(d, 2), # finite numeric
"within_1km": d <= 1000.0,
})
# --- Sibling anchors (3 closest others) ---
sibling_indices = []
for rank in range(1, 4): # skip rank 0 (self)
sib_idx = sib_indices[a_idx][rank]
sibling_indices.append(sib_idx)
# --- Full distance matrix (5 shops x 3 siblings) ---
# rows follow knn order, columns follow sibling distance-ascending order
matrix = []
for s_idx in knn_shop_indices:
row = []
for sib_idx in sibling_indices:
ax = shop_coords[s_idx]
bx = anchor_coords[sib_idx]
d = np.sqrt((ax[0] - bx[0])**2 + (ax[1] - bx[1])**2)
row.append(round(float(d), 2))
matrix.append(row)
results.append({
"anchor_id": anchor_id,
"anchor_name_normalised": anchor_name_norm,
"knn": knn_entries,
"full_distance_matrix_m": matrix,
})
# ── 7. Write JSON ─────────────────────────────────────────────────────────────
with open("/work/market_neighbourhoods.json", "w", encoding="utf-8") as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print("Done. Wrote /work/market_neighbourhoods.json")
print(f"Records: {len(results)}")
# Quick sanity
print(f"First anchor: {results[0]['anchor_id']} — {results[0]['anchor_name_normalised']}")
print(f"KNN shops: {[e['shop_id'] for e in results[0]['knn']]}")
print(f"Matrix shape: {len(results[0]['full_distance_matrix_m'])}x{len(results[0]['full_distance_matrix_m'][0])}")Wrote /work/solve.py (5255 chars).
1:11
Let me run this script.
1:11
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 71, in <module>
from scipy.spatial import cKDTree
ModuleNotFoundError: No module named 'scipy'
1:16
scipy isn't pre-installed. Let me install it, or use an alternative like sklearn's KDTree, or compute manually. Let me install scipy.
1:16
Bash
pip install --user scipy
Collecting scipy Downloading scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB) Requirement already satisfied: numpy<2.7,>=1.26.4 in /usr/local/lib/python3.14/site-packages (from scipy) (2.4.6) Downloading scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (35.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 35.2/35.2 MB 46.6 MB/s 0:00:00 Installing collected packages: scipy Successfully installed scipy-1.17.1
1:26
Bash
cd /work && python solve.py
Done. Wrote /work/market_neighbourhoods.json Records: 100 First anchor: M001 — Tahrir Square Plaza KNN shops: ['S07233', 'S05762', 'S08811', 'S01722', 'S04111'] Matrix shape: 5x3
1:35
Let me verify a few things: 1. The anchor name normalisation — let me check that "Tahrir Square Plaza" is correct from "TAHRIR SQUARE PLAZA". The `str.title()` would do "Tahrir Square Plaza". Good. 2. Let me check some edge cases: - " Maadi Corniche " → strip → "Maadi Corniche" → title → "Maadi Corniche"? Actually str.title() on "Maadi Corniche" gives "Maadi Corniche" which is fine. But wait, "MAADI CORNISH" → strip → "MAADI CORNISH" → title → "Maadi Cornish". Hmm, that changes "MAADI CORNISH" to "Maadi Cornish". Is that correct? The user said "Tidy the anchor names too." Let me look at the data more carefully. Actually, let me check a few more items from anchors to make sure the title-casing works properly. Let me verify a few outputs.
1:35
Bash
python3 -c "
python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Check first record
r = data[0]
print('First record:')
print(json.dumps(r, indent=2, ensure_ascii=False)[:500])
print()
# Check a few anchor names
for r in data:
print(f'{r[\"anchor_id\"]}: {r[\"anchor_name_normalised\"]!r}')
"
First record:
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.58,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.33,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.15,
"within_1km": true
},
{
"shop_id": "S01722
M001: 'Tahrir Square Plaza'
M002: 'Zamalek Riverside'
M003: 'Maadi Corniche'
M004: 'Heliopolis Square'
M005: 'New Cairo Tagamoa'
M006: 'Nasr City Hub'
M007: 'Mohandessin Centre'
M008: 'Garden City Walk'
M009: 'Dokki Market'
M010: 'Giza Pyramids Gate'
M011: 'Roxy Square'
M012: 'Ramses Crossing'
M013: 'Sayeda Zeinab Plaza'
M014: 'Khan El Khalili Approach'
M015: 'Bab Al Louq Corner'
M016: 'Korba Quarter'
M017: 'Manial Riverbank'
M018: 'Shubra North'
M019: 'Ain Shams Plaza'
M020: 'Abbasiya Junction'
M021: 'Boulaq Edge'
M022: 'Garbiya Plaza'
M023: 'Sakakini Approach'
M024: 'Dar El Salaam'
M025: 'El Marg Hub'
M026: 'Helwan Centre'
M027: 'Maasara Crossing'
M028: 'Tora Edge'
M029: 'Mokattam Heights'
M030: 'Nozha Promenade'
M031: 'Sheraton Heliopolis'
M032: 'Triumph Square'
M033: 'Cleopatra Plaza'
M034: 'Salah Salem Strip'
M035: 'Autostrad Corner'
M036: 'El Rehab Gate One'
M037: 'El Rehab Gate Two'
M038: 'Madinaty Promenade'
M039: 'Fifth Settlement North'
M040: 'Fifth Settlement South'
M041: 'American University Gate'
M042: 'Police Academy Strip'
M043: 'Ring Road North'
M044: 'Ring Road East'
M045: 'Ring Road West'
M046: 'City Stars Mall'
M047: 'Cairo Festival City'
M048: 'Mall Of Egypt Gate'
M049: 'Tagamoa First'
M050: 'Tagamoa Third'
M051: 'El Mokattam Plateau'
M052: 'Al Ahly Stadium'
M053: 'Cairo Stadium'
M054: 'Sharkawi Plaza'
M055: 'El Obour Hub'
M056: 'Shoubra Mazallat'
M057: 'Abdeen Palace Edge'
M058: 'El Hussein Square'
M059: 'Al Ghouriya Strip'
M060: 'El Mosky Quarter'
M061: 'Bab Zuweila Approach'
M062: 'Ataba Square'
M063: 'Opera Square'
M064: 'Talaat Harb Plaza'
M065: 'Soliman Pasha Corner'
M066: 'Sherif Street'
M067: 'Qasr El Nile'
M068: 'Kasr El Aini Strip'
M069: 'El Sayeda Aisha'
M070: 'Kobri El Qubba'
M071: 'Mar Mina Plaza'
M072: 'Saint Fatima Hub'
M073: 'El Nozha El Gedida'
M074: 'Rabaa Square'
M075: 'Tagamoa El Saba'
M076: 'Bahteem Crossing'
M077: 'El Salam City'
M078: 'Madinet Nasr Eighth Zone'
M079: 'Madinet Nasr Tenth Zone'
M080: 'El Hadaba El Wosta'
M081: 'Mokattam Sector One'
M082: 'Mokattam Sector Six'
M083: 'El Maadi Degla'
M084: 'Maadi Sarayat'
M085: 'Maadi Cornish'
M086: 'Old Cairo Babylon'
M087: 'Coptic Cairo Plaza'
M088: 'Fustat Park Edge'
M089: 'Manial Bridge'
M090: 'Embaba Crossing'
M091: 'Imbaba Airport Strip'
M092: 'Mit Okba Plaza'
M093: 'El Agouza Riverside'
M094: 'El Sahel Junction'
M095: 'Rod El Farag Bridge'
M096: 'Shubra El Kheima Centre'
M097: 'El Sawah Corner'
M098: 'Demerdash Plaza'
M099: 'El Demerdash Hospital Edge'
M100: 'Ramses Hilton Plaza'
1:43
The output looks good. Let me verify a few more things: 1. Check that the `anchor_name_normalised` fields are non-empty strings — they all look fine. 2. Check that each knn has exactly 5 entries — let me verify. 3. Check that each full_distance_matrix_m has 5 rows and 3 columns. 4. Verify that the normalised names are consistent within shop_ids. 5. Check for Local Shop entries in normalised_name — those should remain as-is since they weren't in the name_map and get filled with the raw_name via fillna. Let me do a few more sanity checks.
1:43
Bash
python3 -c "
python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Validate structure
for r in data:
assert isinstance(r['anchor_id'], str) and r['anchor_id'], f'bad anchor_id'
assert isinstance(r['anchor_name_normalised'], str) and r['anchor_name_normalised'], f'bad name'
assert len(r['knn']) == 5, f'knn not 5: {len(r[\"knn\"])}'
for e in r['knn']:
assert isinstance(e['shop_id'], str) and e['shop_id']
assert isinstance(e['normalised_name'], str) and e['normalised_name']
assert isinstance(e['distance_m'], (int, float))
assert isinstance(e['within_1km'], bool)
# Check ascending distance
for i in range(4):
assert r['knn'][i]['distance_m'] <= r['knn'][i+1]['distance_m'], f'knn not sorted for {r[\"anchor_id\"]}'
# Matrix: 5 rows, 3 columns
assert len(r['full_distance_matrix_m']) == 5, f'matrix rows != 5'
for row in r['full_distance_matrix_m']:
assert len(row) == 3, f'matrix cols != 3'
print('All validations passed!')
print()
# Check that shop_id always maps to same normalised_name
print('Checking shop_id -> name consistency...')
import geopandas as gpd
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
name_map = {
'OSCAR': 'Oscar', 'Oscar Grand Stores': 'Oscar', 'اوسكار': 'Oscar', 'Oscar': 'Oscar',
'Carrefour Egypt': 'Carrefour', 'كارفور': 'Carrefour', 'Carrefour': 'Carrefour', 'carrefour': 'Carrefour',
'metro': 'Metro', 'Metro Markets': 'Metro', 'مترو': 'Metro', 'Metro Market': 'Metro',
'هايبر وان': 'Hyper One', 'Hyper One': 'Hyper One', 'hyperone': 'Hyper One', 'HyperOne': 'Hyper One',
'spinneys cairo': 'Spinneys', 'سبينيز': 'Spinneys', 'Spinneys': 'Spinneys', 'Spineys': 'Spinneys',
'Seoudi': 'Seoudi', 'Seoudi Market': 'Seoudi', 'سعودي': 'Seoudi', 'seoudi supermarket': 'Seoudi',
'Khair Zaman': 'Kheir Zaman', 'kheir zaman': 'Kheir Zaman', 'خير زمان': 'Kheir Zaman', 'Kheir Zaman': 'Kheir Zaman',
'abou zekry': 'Abu Zekry', 'Abu Zikri': 'Abu Zekry', 'أبو زكري': 'Abu Zekry', 'Abu Zekry': 'Abu Zekry',
}
shops['norm'] = shops['raw_name'].map(name_map).fillna(shops['raw_name'])
# Check consistency
grp = shops.groupby('shop_id')['norm'].nunique()
inconsistent = grp[grp > 1]
if len(inconsistent) > 0:
print(f'INCONSISTENT shop_ids: {len(inconsistent)}')
else:
print('All shop_id -> normalised_name mappings are consistent.')
"
All validations passed! Checking shop_id -> name consistency... All shop_id -> normalised_name mappings are consistent.
1:50
Let me also verify the distance matrix rows follow the knn order and columns follow sibling anchor order. Let me also verify the output looks correct for a specific anchor.
1:50
1:50
Bash
python3 -c "
python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Test: ensure anchor_name_normalised is non-empty
for r in data:
name = r['anchor_name_normalised']
assert isinstance(name, str) and len(name) > 0, f'Empty name for {r[\"anchor_id\"]}'
# Test: within_1km matches distance_m / 1000 boundary
for r in data:
for e in r['knn']:
expected = e['distance_m'] <= 1000.0
assert e['within_1km'] == expected, f'{r[\"anchor_id\"]} shop {e[\"shop_id\"]}: d={e[\"distance_m\"]} within_1km={e[\"within_1km\"]} expected={expected}'
# Test: matrix has all finite numeric values
for r in data:
for row in r['full_distance_matrix_m']:
for v in row:
assert isinstance(v, (int, float)) and np.isfinite(v), f'bad matrix value: {v}'
import numpy as np
print('All checks passed!')
print(f'Total records: {len(data)}')
print(f'File size: ', end='')
import os
print(f'{os.path.getsize(\"/work/market_neighbourhoods.json\")} bytes')
"
Traceback (most recent call last):
File "<string>", line 21, in <module>
assert isinstance(v, (int, float)) and np.isfinite(v), f'bad matrix value: {v}'
^^
NameError: name 'np' is not defined
1:56
Small bug in the test script - import numpy. Let me fix that.
1:56
Bash
python3 -c "
python3 -c "
import json, os, numpy as np
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Test: ensure anchor_name_normalised is non-empty
for r in data:
name = r['anchor_name_normalised']
assert isinstance(name, str) and len(name) > 0, f'Empty name for {r[\"anchor_id\"]}'
# Test: within_1km matches distance_m / 1000 boundary
for r in data:
for e in r['knn']:
expected = e['distance_m'] <= 1000.0
assert e['within_1km'] == expected, f'{r[\"anchor_id\"]} shop {e[\"shop_id\"]}: d={e[\"distance_m\"]} within_1km={e[\"within_1km\"]} expected={expected}'
# Test: matrix has all finite numeric values
for r in data:
for row in r['full_distance_matrix_m']:
for v in row:
assert isinstance(v, (int, float)) and np.isfinite(v), f'bad matrix value: {v}'
print('All checks passed!')
print(f'Total records: {len(data)}')
print(f'File size: {os.path.getsize(\"/work/market_neighbourhoods.json\")} bytes')
"
All checks passed! Total records: 100 File size: 120426 bytes
2:06
Let me also verify that the matrix rows follow knn order and columns correspond to sibling anchors in distance-ascending order. Let me spot-check one record.
2:06
2:06
Bash
python3 -c "
python3 -c "
import geopandas as gpd
import numpy as np
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')
# Spot-check anchor 0 (M001)
a_idx = 0
a_geom = anchors.iloc[a_idx].geometry
print(f'Anchor: {anchors.iloc[a_idx][\"anchor_id\"]}')
# Recompute KNN manually to verify
from scipy.spatial import cKDTree
shop_coords = np.array([(p.x, p.y) for p in shops.geometry])
anchor_coords = np.array([(p.x, p.y) for p in anchors.geometry])
tree = cKDTree(shop_coords)
d, idx = tree.query(anchor_coords[a_idx:a_idx+1], k=5)
print(f'KNN shop_ids: {[shops.iloc[i][\"shop_id\"] for i in idx[0]]}')
print(f'KNN distances: {d[0].tolist()}')
# Now verify sibling anchors
anchor_tree = cKDTree(anchor_coords)
sd, sidx = anchor_tree.query(anchor_coords[a_idx:a_idx+1], k=4)
print(f'Sibling anchor ids: {[anchors.iloc[i][\"anchor_id\"] for i in sidx[0][1:4]]}')
print(f'Sibling distances: {sd[0][1:4].tolist()}')
# Now verify the matrix: for shop S07233 (idx[0][0]), compute distance to each sibling
sib_idxs = sidx[0][1:4]
shop_coord = shop_coords[idx[0][0]]
for sib_i in sib_idxs:
sib_coord = anchor_coords[sib_i]
d2 = np.sqrt((shop_coord[0]-sib_coord[0])**2 + (shop_coord[1]-sib_coord[1])**2)
print(f' Shop to {anchors.iloc[sib_i][\"anchor_id\"]}: {d2:.2f} m')
"
Anchor: M001 KNN shop_ids: ['S07233', 'S05762', 'S08811', 'S01722', 'S04111'] KNN distances: [565.5774729861073, 722.3301477157331, 851.1481358488758, 852.4416831354031, 868.96252213481] Sibling anchor ids: ['M002', 'M011', 'M012'] Sibling distances: [1930.504014802109, 2217.0433998541985, 2939.6234099379635] Shop to M002: 2487.01 m Shop to M011: 2174.32 m Shop to M012: 3254.51 m
2:13
Bash
python3 -c "
python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
r = data[0]
print('anchor_id:', r['anchor_id'])
print('anchor_name_normalised:', r['anchor_name_normalised'])
print()
print('KNN:')
for e in r['knn']:
print(f' {e[\"shop_id\"]:6s} {e[\"normalised_name\"]:15s} {e[\"distance_m\"]:8.2f}m within_1km={e[\"within_1km\"]}')
print()
print('Distance matrix (5 rows x 3 cols):')
for i, row in enumerate(r['full_distance_matrix_m']):
print(f' Shop {i} (from knn): {row}')
print()
# Verify: first row should be distances from first KNN shop to siblings M002, M011, M012
# Our manual calc: shop S07233 -> M002=2487.01, M011=2174.32, M012=3254.51
# But the siblings are ordered by distance-ascending: M011 (2174), M002 (2487), M012 (3255)
print('Expected first row (S07233 -> M011, M002, M012 by ascending sib distance):')
print(' ~ [2174.32, 2487.01, 3254.51]')
print(f' Actual: {r[\"full_distance_matrix_m\"][0]}')
"
anchor_id: M001 anchor_name_normalised: Tahrir Square Plaza KNN: S07233 Seoudi 565.58m within_1km=True S05762 Oscar 722.33m within_1km=True S08811 Seoudi 851.15m within_1km=True S01722 Local Shop 01722 852.44m within_1km=True S04111 Local Shop 04111 868.96m within_1km=True Distance matrix (5 rows x 3 cols): Shop 0 (from knn): [2487.01, 2174.32, 3254.51] Shop 1 (from knn): [1534.48, 1756.65, 2217.73] Shop 2 (from knn): [1083.48, 2432.54, 2523.22] Shop 3 (from knn): [1080.92, 2423.44, 2512.91] Shop 4 (from knn): [2589.65, 1669.14, 2955.68] Expected first row (S07233 -> M011, M002, M012 by ascending sib distance): ~ [2174.32, 2487.01, 3254.51] Actual: [2487.01, 2174.32, 3254.51]
2:23
Hmm, let me re-read the spec: "columns correspond to the sibling anchors in distance-ascending order". The sibling anchors are supposed to be the 3 closest sibling anchors to the anchor (not to the shop). Let me check: From the manual verification: - M002 (Zamalek Riverside) is 1930.50 m from M001 - M011 (Roxy Square) is 2217.04 m from M001 - M012 (Ramses Crossing) is 2939.62 m from M001 So in distance-ascending order from the anchor: M002 (1930m), M011 (2217m), M012 (2939m). Looking at the matrix first row: [2487.01, 2174.32, 3254.51] - Column 1 (2487.01) = distance from S07233 to M002 ✓ - Column 2 (2174.32) = distance from S07233 to M011 ✓ - Column 3 (3254.51) = distance from S07233 to M012 ✓ But wait, the columns should be in "sibling anchors in distance-ascending order". The siblings in distance-ascending order from M001 are: M002 (1930m), M011 (2217m), M012 (2939m). But in my code, I used `sibling_indices` which are `sib_indices[a_idx][1:4]` - these are already in distance-ascending order from the anchor. Let me check: - sib_indices[0][1] = index of second closest anchor to M001 = M002 - sib_indices[0][2] = index of third closest anchor to M001 = M011 - sib_indices[0][3] = index of fourth closest anchor to M001 = M012 So the columns are in order: M002, M011, M012. First row: [2487.01, 2174.32, 3254.51] - Col 1 = distance S07233 -> M002 = 2487.01 ✓ - Col 2 = distance S07233 -> M011 = 2174.32 ✓ - Col 3 = distance S07233 -> M012 = 3254.51 ✓ This looks correct! The spec says "columns correspond to the sibling anchors in distance-ascending order". The siblings are ordered by their distance to the anchor (the query anchor). And the matrix cells contain the distance from the shop (row) to the sibling anchor (column). This is correct. Let me verify one more spot check on a different record.
2:23
2:23
Bash
python3 -c "
python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Check record 50
r = data[49]
print(f'{r[\"anchor_id\"]}: {r[\"anchor_name_normalised\"]}')
print('KNN:')
for e in r['knn']:
print(f' {e[\"shop_id\"]:6s} {e[\"normalised_name\"]:20s} {e[\"distance_m\"]:8.2f}m within_1km={e[\"within_1km\"]}')
print(f'Matrix shape: {len(r[\"full_distance_matrix_m\"])}x{len(r[\"full_distance_matrix_m\"][0])}')
print()
# Verify distance ordering in knn
for i in range(len(data)):
knn = data[i]['knn']
for j in range(len(knn)-1):
if knn[j]['distance_m'] > knn[j+1]['distance_m']:
print(f'ERROR: {data[i][\"anchor_id\"]} knn not sorted at pos {j}')
if knn[j]['within_1km'] and knn[j]['distance_m'] > 1000:
print(f'ERROR: {data[i][\"anchor_id\"]} within_1km but d={knn[j][\"distance_m\"]}')
if not knn[j]['within_1km'] and knn[j]['distance_m'] <= 1000:
print(f'ERROR: {data[i][\"anchor_id\"]} not within_1km but d={knn[j][\"distance_m\"]}')
print('All ordering checks passed.')
print(f'File is valid JSON with {len(data)} records.')
"
M050: Tagamoa Third KNN: S08198 Spinneys 192.37m within_1km=True S06874 Spinneys 526.39m within_1km=True S00883 Hyper One 631.82m within_1km=True S03165 Local Shop 03165 722.30m within_1km=True S03197 Carrefour 786.02m within_1km=True Matrix shape: 5x3 All ordering checks passed. File is valid JSON with 100 records.
2:28
Everything looks good. Let me also check the "Local Shop" entries — they have consistent names already (they are individual shops, not chains). The requirement says "the same shop_id always carries the same normalised_name" — since Local Shop 00002 only appears once, that's trivially true. And "all shops belonging to one chain share a single canonical spelling" — the chain names are all normalised as specified in the name_map. Let me verify the output file exists and is well-formed.
2:28
Bash
ls -lh /work/market_neighbourhoods.json
-rw-r--r-- 1 agent agent 118K Jun 18 01:58 /work/market_neighbourhoods.json
2:35
All checks pass. Let me also verify that the `full_distance_matrix_m` columns correspond to sibling anchors ordered by their distance from the query anchor (ascending). Wait - the spec says "columns correspond to the sibling anchors in distance-ascending order". This means the sibling anchors themselves are ordered by their distance from the query anchor, ascending. I used `sib_indices[a_idx][1:4]` which are sorted by distance from `a_idx` ascending (since `query` returns results sorted by distance). So that's correct. Let me also make sure the matrix rows follow knn order. Yes, they do - I iterate over `knn_shop_indices` which are the indices in the order returned by KNN (ascending distance from anchor). So rows = knn order, columns = sibling distance-ascending order. Good. One more thing - let me verify that for the `within_1km` boundary the spec says "true when distance_m is at most 1000". My code uses `d <= 1000.0`. Let me double-check this against the spec: "within_1km (boolean, true when distance_m is at most 1000)". Yes, `<= 1000`. Good. Now let me verify the output file's JSON is actually a top-level array (not an object).
2:35
2:35
Bash
python3 -c "
python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
content = f.read()
# Check it starts with [
print('Starts with [:', content.strip()[:1] == '[')
print('Ends with ]:', content.strip()[-1:] == ']')
data = json.loads(content)
print('Type:', type(data).__name__)
print('Length:', len(data))
print('All have required keys:',
all(set(d.keys()) == {'anchor_id', 'anchor_name_normalised', 'knn', 'full_distance_matrix_m'}
for d in data))
print('All anchor_name_normalised non-empty:',
all(isinstance(d['anchor_name_normalised'], str) and len(d['anchor_name_normalised'].strip()) > 0
for d in data))
print('All knn exactly 5 entries:',
all(len(d['knn']) == 5 for d in data))
print('All knn sorted ascending:',
all(d['knn'][i]['distance_m'] <= d['knn'][i+1]['distance_m']
for d in data for i in range(4)))
print('All within_1km correct:',
all(d['knn'][i]['within_1km'] == (d['knn'][i]['distance_m'] <= 1000.0)
for d in data for i in range(5)))
print('All matrices 5x3:',
all(len(d['full_distance_matrix_m']) == 5 and
all(len(row) == 3 for row in d['full_distance_matrix_m'])
for d in data))
"
Starts with [: True Ends with ]: True Type: list Length: 100 All have required keys: True All anchor_name_normalised non-empty: True All knn exactly 5 entries: True All knn sorted ascending: True All within_1km correct: True All matrices 5x3: True
2:41
Everything validates perfectly. Let me summarize what was done.
2:41