dc-l1-bangkok-attribute-coercion
Map (reference ← swipe → agent)
0:00
Our analytics dashboard computes averages from the vendor's bangkok_aq_stations export and the numbers are coming back wrong. The means show up as NaN and the city director keeps getting figures that do not reconcile. Please investigate the file and fix whatever is keeping the numeric computations from working. Leave the string columns and the geometry alone, and keep Thai script in any string field exactly as it arrives. Write the result to bangkok_aq_typed.geojson.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:10
The user has a file called `bangkok_aq_stations` (likely `bangkok_aq_stations.geojson` or similar) and they're getting NaN values when computing averages. This suggests the numeric columns are being read as strings or some other non-numeric type. I need to: 1. First, find the exact filename 2. Investigate its contents 3. Fix the numeric columns so they're proper numeric types 4. Write to `bangkok_aq_typed.geojson` Let me start by looking at what files exist.
0:10
Bash
ls /work/
bangkok_aq_stations.geojson
0:14
Let me first look at the file to understand its structure and identify the problem.
0:14
Read
/work/bangkok_aq_stations.geojson
1 {
2 "type": "FeatureCollection",
3 "name": "bangkok_aq_stations",
4 "crs": {
5 "type": "name",
6 "properties": {
7 "name": "urn:ogc:def:crs:OGC:1.3:CRS84"
8 }
9 },
10 "features": [
11 {
12 "type": "Feature",
13 "geometry": {
14 "type": "Point",
15 "coordinates": [
16 100.5294,
17 13.7464
18 ]
19 },
20 "properties": {
21 "station_id": "1",
22 "name_th": "สนามกีฬาแห่งชาติ",
23 "name_en": "National Stadium",
24 "sensor_value": "84.55",
25 "pm25_ug_m3": "63.6",
26 "elevation_m": "11.0"
27 }
28 },
29 {
30 "type": "Feature",
31 "geometry": {
32 "type": "Point",
33 "coordinates": [
34 100.5343,
35 13.7456
36 ]
37 },
38 "properties": {
39 "station_id": "2",
40 "name_th": "สยาม",
41 "name_en": "Siam",
42 "sensor_value": "87.00",
43 "pm25_ug_m3": "60.5",
44 "elevation_m": "12.5"
45 }
46 },
47 {
48 "type": "Feature",
49 "geometry": {
50 "type": "Point",
51 "coordinates": [
52 100.5436,
53 13.7441
54 ]
55 },
56 "properties": {
57 "station_id": "3",
58 "name_th": "ชิดลม",
59 "name_en": "Chit Lom",
60 "sensor_value": "80.95",
61 "pm25_ug_m3": "54.8",
62 "elevation_m": "14.0"
63 }
64 },
65 {
66 "type": "Feature",
67 "geometry": {
68 "type": "Point",
69 "coordinates": [
70 100.5485,
71 13.7434
72 ]
73 },
74 "properties": {
75 "station_id": "4",
76 "name_th": "เพลินจิต",
77 "name_en": "Phloen Chit",
78 "sensor_value": "73.94",
79 "pm25_ug_m3": "49.0",
80 "elevation_m": "15.4"
<system-reminder>Showed lines 1-80 of 1812. Re-call Read with offset=81 to continue, or raise limit.</system-reminder>
0:25
The issue is clear - the numeric columns (`sensor_value`, `pm25_ug_m3`, `elevation_m`) are stored as strings in the GeoJSON. When computing averages, they become NaN because string values can't be averaged. I need to read the whole file, convert those numeric columns to actual numbers, while leaving string columns (`station_id`, `name_th`, `name_en`) and geometry untouched. Let me check the rest of the file first, then write the fix.
0:25
Read
/work/bangkok_aq_stations.geojson
81 }
82 },
83 {
84 "type": "Feature",
85 "geometry": {
86 "type": "Point",
87 "coordinates": [
88 100.555,
89 13.7407
90 ]
91 },
92 "properties": {
93 "station_id": "5",
94 "name_th": "นานา",
95 "name_en": "Nana",
96 "sensor_value": "72.10",
97 "pm25_ug_m3": "45.6",
98 "elevation_m": "16.7"
99 }
100 },
101 {
102 "type": "Feature",
103 "geometry": {
104 "type": "Point",
105 "coordinates": [
106 100.5604,
107 13.7373
108 ]
109 },
110 "properties": {
111 "station_id": "6",
112 "name_th": "อโศก",
113 "name_en": "Asok",
114 "sensor_value": "75.29",
115 "pm25_ug_m3": "45.1",
116 "elevation_m": "17.8"
117 }
118 },
119 {
120 "type": "Feature",
121 "geometry": {
122 "type": "Point",
123 "coordinates": [
124 100.5694,
125 13.7305
126 ]
127 },
128 "properties": {
129 "station_id": "7",
130 "name_th": "พร้อมพงษ์",
131 "name_en": "Phrom Phong",
132 "sensor_value": "77.39",
133 "pm25_ug_m3": "46.7",
134 "elevation_m": "18.7"
135 }
136 },
137 {
138 "type": "Feature",
139 "geometry": {
140 "type": "Point",
141 "coordinates": [
142 100.578,
143 13.724
144 ]
145 },
146 "properties": {
147 "station_id": "8",
148 "name_th": "ทองหล่อ",
149 "name_en": "Thong Lo",
150 "sensor_value": "71.34",
151 "pm25_ug_m3": "47.9",
152 "elevation_m": "19.4"
153 }
154 },
155 {
156 "type": "Feature",
157 "geometry": {
158 "type": "Point",
159 "coordinates": [
160 100.5852,
<system-reminder>Showed lines 81-160 of 1812. Re-call Read with offset=161 to continue, or raise limit.</system-reminder>
0:33
I can see the problem clearly. The numeric fields like `sensor_value`, `pm25_ug_m3`, and `elevation_m` are stored as strings (e.g., `"84.55"` instead of `84.55`). When the analytics dashboard tries to compute averages, it's trying to do math on strings, resulting in NaN. I need to convert these numeric string fields to actual numbers while leaving string columns (station_id, name_th, name_en) and geometry untouched. Let me write a Python script using geopandas to do this.
0:33
0:33
Write
/work/solve.py
content · 35 lines · py
import geopandas as gpd
import json
# Read the input
gdf = gpd.read_file("/work/bangkok_aq_stations.geojson")
# Identify numeric columns — fields that every value can be cast to float
# We'll be conservative: only convert columns where every non-null value is numeric
numeric_cols = []
for col in gdf.columns:
if col in ("station_id", "name_th", "name_en", "geometry"):
continue
# Try converting all values to float; if any fails, skip
try:
gdf[col].astype(float)
numeric_cols.append(col)
except (ValueError, TypeError):
pass
print("Numeric columns detected:", numeric_cols)
# Convert each numeric column to proper float64
for col in numeric_cols:
gdf[col] = gdf[col].astype(float)
# Write output — ensure Thai script is preserved
gdf.to_file("/work/bangkok_aq_typed.geojson", driver="GeoJSON", encoding="utf-8")
# Quick sanity check
print("Data types after conversion:")
print(gdf.dtypes)
print()
print("Mean sensor_value:", gdf["sensor_value"].mean())
print("Mean pm25_ug_m3:", gdf["pm25_ug_m3"].mean())
print("Mean elevation_m:", gdf["elevation_m"].mean())Wrote /work/solve.py (1117 chars).
0:36
Let me run this script to see the results.
0:36
Bash
cd /work && python solve.py
Numeric columns detected: ['sensor_value', 'pm25_ug_m3', 'elevation_m'] Data types after conversion: station_id str name_th str name_en str sensor_value float64 pm25_ug_m3 float64 elevation_m float64 geometry geometry dtype: object Mean sensor_value: 49.97830000000001 Mean pm25_ug_m3: 36.516000000000005 Mean elevation_m: 11.716000000000001
0:43
The script ran successfully. Let me verify the output file exists and check a few values to make sure the Thai text is preserved.
0:43
Bash
cd /work && ls -la bangkok_aq_typed.geojson
-rw-r--r-- 1 agent agent 28136 Jun 16 16:38 bangkok_aq_typed.geojson
0:43
Read
/work/bangkok_aq_typed.geojson
1 {
2 "type": "FeatureCollection",
3 "name": "bangkok_aq_typed",
4 "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
5 "features": [
6 { "type": "Feature", "properties": { "station_id": "1", "name_th": "สนามกีฬาแห่งชาติ", "name_en": "National Stadium", "sensor_value": 84.55, "pm25_ug_m3": 63.6, "elevation_m": 11.0 }, "geometry": { "type": "Point", "coordinates": [ 100.5294, 13.7464 ] } },
7 { "type": "Feature", "properties": { "station_id": "2", "name_th": "สยาม", "name_en": "Siam", "sensor_value": 87.0, "pm25_ug_m3": 60.5, "elevation_m": 12.5 }, "geometry": { "type": "Point", "coordinates": [ 100.5343, 13.7456 ] } },
8 { "type": "Feature", "properties": { "station_id": "3", "name_th": "ชิดลม", "name_en": "Chit Lom", "sensor_value": 80.95, "pm25_ug_m3": 54.8, "elevation_m": 14.0 }, "geometry": { "type": "Point", "coordinates": [ 100.5436, 13.7441 ] } },
9 { "type": "Feature", "properties": { "station_id": "4", "name_th": "เพลินจิต", "name_en": "Phloen Chit", "sensor_value": 73.94, "pm25_ug_m3": 49.0, "elevation_m": 15.4 }, "geometry": { "type": "Point", "coordinates": [ 100.5485, 13.7434 ] } },
10 { "type": "Feature", "properties": { "station_id": "5", "name_th": "นานา", "name_en": "Nana", "sensor_value": 72.1, "pm25_ug_m3": 45.6, "elevation_m": 16.7 }, "geometry": { "type": "Point", "coordinates": [ 100.555, 13.7407 ] } },
11 { "type": "Feature", "properties": { "station_id": "6", "name_th": "อโศก", "name_en": "Asok", "sensor_value": 75.29, "pm25_ug_m3": 45.1, "elevation_m": 17.8 }, "geometry": { "type": "Point", "coordinates": [ 100.5604, 13.7373 ] } },
12 { "type": "Feature", "properties": { "station_id": "7", "name_th": "พร้อมพงษ์", "name_en": "Phrom Phong", "sensor_value": 77.39, "pm25_ug_m3": 46.7, "elevation_m": 18.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5694, 13.7305 ] } },
13 { "type": "Feature", "properties": { "station_id": "8", "name_th": "ทองหล่อ", "name_en": "Thong Lo", "sensor_value": 71.34, "pm25_ug_m3": 47.9, "elevation_m": 19.4 }, "geometry": { "type": "Point", "coordinates": [ 100.578, 13.724 ] } },
14 { "type": "Feature", "properties": { "station_id": "9", "name_th": "เอกมัย", "name_en": "Ekkamai", "sensor_value": 55.17, "pm25_ug_m3": 46.4, "elevation_m": 19.8 }, "geometry": { "type": "Point", "coordinates": [ 100.5852, 13.7197 ] } },
15 { "type": "Feature", "properties": { "station_id": "10", "name_th": "พระโขนง", "name_en": "Phra Khanong", "sensor_value": 34.09, "pm25_ug_m3": 40.9, "elevation_m": 20.0 }, "geometry": { "type": "Point", "coordinates": [ 100.5916, 13.7152 ] } },
16 { "type": "Feature", "properties": { "station_id": "11", "name_th": "อ่อนนุช", "name_en": "On Nut", "sensor_value": 17.21, "pm25_ug_m3": 32.2, "elevation_m": 19.9 }, "geometry": { "type": "Point", "coordinates": [ 100.6014, 13.7058 ] } },
17 { "type": "Feature", "properties": { "station_id": "12", "name_th": "บางจาก", "name_en": "Bang Chak", "sensor_value": 11.16, "pm25_ug_m3": 22.3, "elevation_m": 19.6 }, "geometry": { "type": "Point", "coordinates": [ 100.6053, 13.6963 ] } },
18 { "type": "Feature", "properties": { "station_id": "13", "name_th": "ปุณณวิถี", "name_en": "Punnawithi", "sensor_value": 15.58, "pm25_ug_m3": 14.2, "elevation_m": 19.0 }, "geometry": { "type": "Point", "coordinates": [ 100.6106, 13.689 ] } },
19 { "type": "Feature", "properties": { "station_id": "14", "name_th": "อุดมสุข", "name_en": "Udom Suk", "sensor_value": 23.9, "pm25_ug_m3": 10.1, "elevation_m": 18.2 }, "geometry": { "type": "Point", "coordinates": [ 100.6166, 13.6794 ] } },
20 { "type": "Feature", "properties": { "station_id": "15", "name_th": "บางนา", "name_en": "Bang Na", "sensor_value": 28.68, "pm25_ug_m3": 10.5, "elevation_m": 17.2 }, "geometry": { "type": "Point", "coordinates": [ 100.6256, 13.668 ] } },
21 { "type": "Feature", "properties": { "station_id": "16", "name_th": "แบริ่ง", "name_en": "Bearing", "sensor_value": 27.49, "pm25_ug_m3": 14.2, "elevation_m": 16.0 }, "geometry": { "type": "Point", "coordinates": [ 100.6354, 13.661 ] } },
22 { "type": "Feature", "properties": { "station_id": "17", "name_th": "ราชดำริ", "name_en": "Ratchadamri", "sensor_value": 24.65, "pm25_ug_m3": 18.6, "elevation_m": 14.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5403, 13.7398 ] } },
23 { "type": "Feature", "properties": { "station_id": "18", "name_th": "ศาลาแดง", "name_en": "Sala Daeng", "sensor_value": 27.55, "pm25_ug_m3": 21.2, "elevation_m": 13.2 }, "geometry": { "type": "Point", "coordinates": [ 100.5345, 13.7286 ] } },
24 { "type": "Feature", "properties": { "station_id": "19", "name_th": "ช่องนนทรี", "name_en": "Chong Nonsi", "sensor_value": 40.5, "pm25_ug_m3": 21.0, "elevation_m": 11.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5293, 13.7235 ] } },
25 { "type": "Feature", "properties": { "station_id": "20", "name_th": "สุรศักดิ์", "name_en": "Surasak", "sensor_value": 60.71, "pm25_ug_m3": 18.5, "elevation_m": 10.2 }, "geometry": { "type": "Point", "coordinates": [ 100.5223, 13.7197 ] } },
26 { "type": "Feature", "properties": { "station_id": "21", "name_th": "สะพานตากสิน", "name_en": "Saphan Taksin", "sensor_value": 79.8, "pm25_ug_m3": 15.9, "elevation_m": 8.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5147, 13.719 ] } },
27 { "type": "Feature", "properties": { "station_id": "22", "name_th": "หัวลำโพง", "name_en": "Hua Lamphong", "sensor_value": 89.59, "pm25_ug_m3": 15.7, "elevation_m": 7.3 }, "geometry": { "type": "Point", "coordinates": [ 100.517, 13.7378 ] } },
28 { "type": "Feature", "properties": { "station_id": "23", "name_th": "สามย่าน", "name_en": "Sam Yan", "sensor_value": 87.74, "pm25_ug_m3": 19.6, "elevation_m": 5.9 }, "geometry": { "type": "Point", "coordinates": [ 100.529, 13.7325 ] } },
29 { "type": "Feature", "properties": { "station_id": "24", "name_th": "สีลม", "name_en": "Si Lom", "sensor_value": 79.14, "pm25_ug_m3": 27.5, "elevation_m": 4.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5346, 13.729 ] } },
30 { "type": "Feature", "properties": { "station_id": "25", "name_th": "ลุมพินี", "name_en": "Lumphini", "sensor_value": 71.71, "pm25_ug_m3": 37.5, "elevation_m": 3.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5443, 13.7253 ] } },
31 { "type": "Feature", "properties": { "station_id": "26", "name_th": "คลองเตย", "name_en": "Khlong Toei", "sensor_value": 70.26, "pm25_ug_m3": 46.9, "elevation_m": 2.9 }, "geometry": { "type": "Point", "coordinates": [ 100.5545, 13.7223 ] } },
32 { "type": "Feature", "properties": { "station_id": "27", "name_th": "ศูนย์การประชุมแห่งชาติสิริกิติ์", "name_en": "Queen Sirikit Centre", "sensor_value": 72.77, "pm25_ug_m3": 53.1, "elevation_m": 2.4 }, "geometry": { "type": "Point", "coordinates": [ 100.5579, 13.7233 ] } },
33 { "type": "Feature", "properties": { "station_id": "28", "name_th": "สุขุมวิท", "name_en": "Sukhumvit", "sensor_value": 72.31, "pm25_ug_m3": 55.0, "elevation_m": 2.1 }, "geometry": { "type": "Point", "coordinates": [ 100.5604, 13.7378 ] } },
34 { "type": "Feature", "properties": { "station_id": "29", "name_th": "เพชรบุรี", "name_en": "Phetchaburi", "sensor_value": 62.85, "pm25_ug_m3": 53.3, "elevation_m": 2.0 }, "geometry": { "type": "Point", "coordinates": [ 100.5635, 13.7479 ] } },
35 { "type": "Feature", "properties": { "station_id": "30", "name_th": "พระราม 9", "name_en": "Phra Ram 9", "sensor_value": 44.53, "pm25_ug_m3": 50.4, "elevation_m": 2.2 }, "geometry": { "type": "Point", "coordinates": [ 100.5654, 13.757 ] } },
36 { "type": "Feature", "properties": { "station_id": "31", "name_th": "ศูนย์วัฒนธรรมแห่งประเทศไทย", "name_en": "Thailand Cultural Centre", "sensor_value": 24.22, "pm25_ug_m3": 48.6, "elevation_m": 2.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5701, 13.7665 ] } },
37 { "type": "Feature", "properties": { "station_id": "32", "name_th": "ห้วยขวาง", "name_en": "Huai Khwang", "sensor_value": 10.93, "pm25_ug_m3": 49.7, "elevation_m": 3.4 }, "geometry": { "type": "Point", "coordinates": [ 100.5736, 13.777 ] } },
38 { "type": "Feature", "properties": { "station_id": "33", "name_th": "สุทธิสาร", "name_en": "Sutthisan", "sensor_value": 9.48, "pm25_ug_m3": 53.3, "elevation_m": 4.3 }, "geometry": { "type": "Point", "coordinates": [ 100.5747, 13.7886 ] } },
39 { "type": "Feature", "properties": { "station_id": "34", "name_th": "รัชดาภิเษก", "name_en": "Ratchadaphisek", "sensor_value": 17.28, "pm25_ug_m3": 57.6, "elevation_m": 5.4 }, "geometry": { "type": "Point", "coordinates": [ 100.574, 13.7995 ] } },
40 { "type": "Feature", "properties": { "station_id": "35", "name_th": "ลาดพร้าว", "name_en": "Lat Phrao", "sensor_value": 26.71, "pm25_ug_m3": 60.0, "elevation_m": 6.7 }, "geometry": { "type": "Point", "coordinates": [ 100.574, 13.8062 ] } },
41 { "type": "Feature", "properties": { "station_id": "36", "name_th": "พหลโยธิน", "name_en": "Phahon Yothin", "sensor_value": 31.13, "pm25_ug_m3": 58.3, "elevation_m": 8.1 }, "geometry": { "type": "Point", "coordinates": [ 100.5618, 13.8141 ] } },
42 { "type": "Feature", "properties": { "station_id": "37", "name_th": "สวนจตุจักร", "name_en": "Chatuchak Park", "sensor_value": 29.98, "pm25_ug_m3": 52.2, "elevation_m": 9.5 }, "geometry": { "type": "Point", "coordinates": [ 100.5535, 13.8138 ] } },
43 { "type": "Feature", "properties": { "station_id": "38", "name_th": "กำแพงเพชร", "name_en": "Kamphaeng Phet", "sensor_value": 28.91, "pm25_ug_m3": 42.9, "elevation_m": 11.1 }, "geometry": { "type": "Point", "coordinates": [ 100.5495, 13.8027 ] } },
44 { "type": "Feature", "properties": { "station_id": "39", "name_th": "บางซื่อ", "name_en": "Bang Sue", "sensor_value": 34.98, "pm25_ug_m3": 33.2, "elevation_m": 12.6 }, "geometry": { "type": "Point", "coordinates": [ 100.5365, 13.8027 ] } },
45 { "type": "Feature", "properties": { "station_id": "40", "name_th": "เตาปูน", "name_en": "Tao Poon", "sensor_value": 50.61, "pm25_ug_m3": 25.7, "elevation_m": 14.1 }, "geometry": { "type": "Point", "coordinates": [ 100.5289, 13.8055 ] } },
46 { "type": "Feature", "properties": { "station_id": "41", "name_th": "บางซ่อน", "name_en": "Bang Son", "sensor_value": 71.02, "pm25_ug_m3": 22.1, "elevation_m": 15.4 }, "geometry": { "type": "Point", "coordinates": [ 100.524, 13.8113 ] } },
47 { "type": "Feature", "properties": { "station_id": "42", "name_th": "วงศ์สว่าง", "name_en": "Wong Sawang", "sensor_value": 87.25, "pm25_ug_m3": 22.1, "elevation_m": 16.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5197, 13.8195 ] } },
48 { "type": "Feature", "properties": { "station_id": "43", "name_th": "บางพลัด", "name_en": "Bang Phlat", "sensor_value": 92.41, "pm25_ug_m3": 23.8, "elevation_m": 17.8 }, "geometry": { "type": "Point", "coordinates": [ 100.505, 13.7935 ] } },
49 { "type": "Feature", "properties": { "station_id": "44", "name_th": "พญาไท", "name_en": "Phaya Thai", "sensor_value": 86.47, "pm25_ug_m3": 24.8, "elevation_m": 18.7 }, "geometry": { "type": "Point", "coordinates": [ 100.5341, 13.7568 ] } },
50 { "type": "Feature", "properties": { "station_id": "45", "name_th": "ราชปรารภ", "name_en": "Ratchaprarop", "sensor_value": 75.93, "pm25_ug_m3": 23.0, "elevation_m": 19.4 }, "geometry": { "type": "Point", "coordinates": [ 100.5413, 13.7566 ] } },
<system-reminder>Showed lines 1-50 of 107. Re-call Read with offset=51 to continue, or raise limit.</system-reminder>
0:56
The output looks correct. Let me verify a few things: 1. The numeric fields (`sensor_value`, `pm25_ug_m3`, `elevation_m`) are now stored as actual numbers (no quotes in JSON) — e.g., `84.55` instead of `"84.55"`. 2. The string columns (`station_id`, `name_th`, `name_en`) remain as strings with quotes. 3. The Thai script is preserved (e.g., `"สนามกีฬาแห่งชาติ"`). 4. The geometry is unchanged. 5. The CRS is preserved. Let me also check the means match what the script computed, and verify the file size and existence.
0:56
0:56
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('bangkok_aq_typed.geojson')
print('Feature count:', len(gdf))
print('CRS:', gdf.crs)
print('Columns:', list(gdf.columns))
for col in gdf.columns:
print(f' {col}: dtype={gdf[col].dtype}, sample={gdf[col].iloc[0]}')
print()
print('Means:')
print(f' sensor_value: {gdf[\"sensor_value\"].mean()}')
print(f' pm25_ug_m3: {gdf[\"pm25_ug_m3\"].mean()}')
print(f' elevation_m: {gdf[\"elevation_m\"].mean()}')
"
Feature count: 100 CRS: EPSG:4326 Columns: ['station_id', 'name_th', 'name_en', 'sensor_value', 'pm25_ug_m3', 'elevation_m', 'geometry'] station_id: dtype=str, sample=1 name_th: dtype=str, sample=สนามกีฬาแห่งชาติ name_en: dtype=str, sample=National Stadium sensor_value: dtype=float64, sample=84.55 pm25_ug_m3: dtype=float64, sample=63.6 elevation_m: dtype=float64, sample=11.0 geometry: dtype=geometry, sample=POINT (100.5294 13.7464) Means: sensor_value: 49.97830000000001 pm25_ug_m3: 36.516000000000005 elevation_m: 11.716000000000001
1:06
Everything looks good. Let me summarize what I found and did.
1:06