dd-l1-vienna-gpkg-manifest
Map (reference ← swipe → agent)
0:00
Inherited a GPKG (vienna_planning) from a colleague who retired last week. Before I commit to scripting against it I need a one-page manifest so I can see what's in there and decide which layers actually feed next month's councillor briefing. Catalog the contents and give me one record per layer. Output manifest.json — a JSON list of these records.
Each record in the list must be a JSON object with exactly five keys: `layer_name` as a string, `crs` as a string in `EPSG:NNNN` form, `geometry_type` as a string matching the layer's native type (e.g. `Point`, `LineString`, `Polygon`, `MultiPolygon`), `feature_count` as an integer, and `bbox` as a list of four numbers `[xmin, ymin, xmax, ymax]` in the layer's native CRS.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:01
Let me start by exploring the GPKG file to understand its contents.
0:01
Bash
ls -lh /work/vienna_planning*
-rw-r--r-- 1 agent agent 512K Jun 16 17:31 /work/vienna_planning.gpkg
0:31
Let me inspect the GPKG to find all layers, their CRS, geometry types, feature counts, and bounding boxes.
0:31
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import json
# List layers
layers = gpd.list_layers('vienna_planning.gpkg')
print('=== LAYERS ===')
print(layers.to_string())
print()
# For each, read and get metadata
for lyr in layers['name']:
gdf = gpd.read_file('vienna_planning.gpkg', layer=lyr)
crs_str = gdf.crs.to_epsg()
if crs_str:
crs_str = f'EPSG:{crs_str}'
else:
crs_str = str(gdf.crs)
geom_types = gdf.geometry.type.unique().tolist()
feat_count = len(gdf)
bbox = list(gdf.total_bounds) # [xmin, ymin, xmax, ymax]
print(f'Layer: {lyr}')
print(f' CRS: {crs_str}')
print(f' Geometry types: {geom_types}')
print(f' Feature count: {feat_count}')
print(f' BBox: {bbox}')
print()
"
=== LAYERS ===
name geometry_type
0 districts Polygon
1 parks MultiPolygon
2 waterbodies Polygon
3 schools Point
4 cafes Point
5 supermarkets Point
6 cycleway_segments LineString
Layer: districts
CRS: EPSG:31287
Geometry types: ['Polygon']
Feature count: 22
BBox: [np.float64(623867.3888224852), np.float64(481236.5836125494), np.float64(626371.3079994549), np.float64(484403.7315526943)]
Layer: parks
CRS: EPSG:31287
Geometry types: ['MultiPolygon']
Feature count: 119
BBox: [np.float64(623851.990195953), np.float64(481200.6674012489), np.float64(626620.9438865517), np.float64(485539.3479671855)]
Layer: waterbodies
CRS: EPSG:31287
Geometry types: ['Polygon']
Feature count: 33
BBox: [np.float64(624288.8521056126), np.float64(481236.66320640914), np.float64(626747.8611584986), np.float64(484132.0212685799)]
Layer: schools
CRS: EPSG:31287
Geometry types: ['Point']
Feature count: 40
BBox: [np.float64(624000.002417057), np.float64(481214.73871311604), np.float64(626259.5421406503), np.float64(484178.87865010765)]
Layer: cafes
CRS: EPSG:31287
Geometry types: ['Point']
Feature count: 392
BBox: [np.float64(623796.529863561), np.float64(481107.2540129812), np.float64(626399.5618121764), np.float64(484498.9209005649)]
Layer: supermarkets
CRS: EPSG:31287
Geometry types: ['Point']
Feature count: 87
BBox: [np.float64(623794.0549950565), np.float64(481148.38703723426), np.float64(626392.6164786755), np.float64(484489.91954147833)]
Layer: cycleway_segments
CRS: EPSG:31287
Geometry types: ['LineString']
Feature count: 271
BBox: [np.float64(623787.4932131292), np.float64(481134.9325878215), np.float64(626619.1631922598), np.float64(485245.6624853243)]
0:46
Now I have all the info I need. Let me write the manifest.json file with the required format. Let me check the geometry types from `gpd.list_layers` more carefully - it listed 'Polygon' for districts but the actual type is also 'Polygon'. For parks it says 'MultiPolygon' which matches. Let me use the geometry type from the actual GeoDataFrame since that's the native type. Wait, `gpd.list_layers()` already returned the geometry type column. Let me cross-reference: - districts: geometry_type from list_layers = 'Polygon', actual types = ['Polygon'] → matches - parks: geometry_type = 'MultiPolygon', actual = ['MultiPolygon'] → matches - waterbodies: geometry_type = 'Polygon', actual = ['Polygon'] → matches - schools: geometry_type = 'Point', actual = ['Point'] → matches - cafes: geometry_type = 'Point', actual = ['Point'] → matches - supermarkets: geometry_type = 'Point', actual = ['Point'] → matches - cycleway_segments: geometry_type = 'LineString', actual = ['LineString'] → matches Good, I'll use the geometry_type from the data as instructed. Now let me write the Python script to generate the manifest.json.
0:46
Write
/work/solve.py
content · 33 lines · py
import geopandas as gpd
import json
layers_info = gpd.list_layers("/work/vienna_planning.gpkg")
records = []
for lyr_name in layers_info["name"]:
gdf = gpd.read_file("/work/vienna_planning.gpkg", layer=lyr_name)
epsg = gdf.crs.to_epsg()
crs_str = f"EPSG:{epsg}" if epsg else str(gdf.crs)
geom_type = gdf.geometry.type.unique()
# If multiple geometry types exist, report the native one from list_layers
# but we'll trust the data. Take the first unique type.
geometry_type = str(geom_type[0])
feature_count = len(gdf)
bbox = [float(v) for v in gdf.total_bounds] # [xmin, ymin, xmax, ymax]
records.append({
"layer_name": lyr_name,
"crs": crs_str,
"geometry_type": geometry_type,
"feature_count": feature_count,
"bbox": bbox,
})
with open("/work/manifest.json", "w") as f:
json.dump(records, f, indent=2)
print("Written manifest.json")Wrote /work/solve.py (930 chars).
0:54
Let me run the script and then verify the output.
0:54
Bash
cd /work && python solve.py
Written manifest.json
1:04
Let me verify the output to make sure it's correct.
1:04
Read
/work/manifest.json
1 [
2 {
3 "layer_name": "districts",
4 "crs": "EPSG:31287",
5 "geometry_type": "Polygon",
6 "feature_count": 22,
7 "bbox": [
8 623867.3888224852,
9 481236.5836125494,
10 626371.3079994549,
11 484403.7315526943
12 ]
13 },
14 {
15 "layer_name": "parks",
16 "crs": "EPSG:31287",
17 "geometry_type": "MultiPolygon",
18 "feature_count": 119,
19 "bbox": [
20 623851.990195953,
21 481200.6674012489,
22 626620.9438865517,
23 485539.3479671855
24 ]
25 },
26 {
27 "layer_name": "waterbodies",
28 "crs": "EPSG:31287",
29 "geometry_type": "Polygon",
30 "feature_count": 33,
31 "bbox": [
32 624288.8521056126,
33 481236.66320640914,
34 626747.8611584986,
35 484132.0212685799
36 ]
37 },
38 {
39 "layer_name": "schools",
40 "crs": "EPSG:31287",
41 "geometry_type": "Point",
42 "feature_count": 40,
43 "bbox": [
44 624000.002417057,
45 481214.73871311604,
46 626259.5421406503,
47 484178.87865010765
48 ]
49 },
50 {
51 "layer_name": "cafes",
52 "crs": "EPSG:31287",
53 "geometry_type": "Point",
54 "feature_count": 392,
55 "bbox": [
56 623796.529863561,
57 481107.2540129812,
58 626399.5618121764,
59 484498.9209005649
60 ]
61 },
62 {
63 "layer_name": "supermarkets",
64 "crs": "EPSG:31287",
65 "geometry_type": "Point",
66 "feature_count": 87,
67 "bbox": [
68 623794.0549950565,
69 481148.38703723426,
70 626392.6164786755,
71 484489.91954147833
72 ]
73 },
74 {
75 "layer_name": "cycleway_segments",
76 "crs": "EPSG:31287",
77 "geometry_type": "LineString",
78 "feature_count": 271,
79 "bbox": [
80 623787.4932131292,
81 481134.9325878215,
82 626619.1631922598,
83 485245.6624853243
84 ]
85 }
86 ]
1:15
Everything looks good. Let me verify the output against the requirements: 1. ✅ `manifest.json` file exists in working directory 2. ✅ It's a JSON list (array) of objects 3. ✅ Each object has exactly five keys: `layer_name`, `crs`, `geometry_type`, `feature_count`, `bbox` 4. ✅ `layer_name` is a string 5. ✅ `crs` is in `EPSG:NNNN` format (EPSG:31287 — MGI / Austria Lambert, which is correct for Vienna) 6. ✅ `geometry_type` matches native types (Polygon, MultiPolygon, Point, LineString) 7. ✅ `feature_count` is integer 8. ✅ `bbox` is a list of four numbers [xmin, ymin, xmax, ymax] in native CRS 9. ✅ Coordinate magnitudes are ~600k-700k which is correct for EPSG:31287 (Austria Lambert) — expected for Vienna All good!
1:15