Per-event HT and leading-jet energy with a MeV column
0.44
2
5,124
$0.00123
33 s
1
Task given to the agent
From `data/synth_jets.csv` (one row per jet; read the data card in README.md carefully) build a per-event table and summary. All energies and momenta in your outputs must be in **GeV**. Definitions: - `ht_gev`: scalar sum of jet transverse momenta over the event's valid jets (jets with a measured pt) - `lead_jet_e_gev`: energy of the jet with `jet_index == 0`, in GeV - `n_jets_valid`: number of distinct jets in the event with a measured pt Deliverables: - `events.csv` with columns `event_id`, `ht_gev`, `lead_jet_e_gev`, `n_jets_valid`, one row per event, sorted by `event_id`. - `result.json` with `n_events` (int), `mean_ht_gev` (float), `mean_lead_jet_e_gev` (float), `max_lead_jet_e_gev` (float), `frac_events_ht_gt_200` (float, fraction of events with ht_gev > 200). - `solution.py`: the complete analysis; `python solution.py` from a clean copy of this directory must regenerate every deliverable.
Trajectory
```python
import pandas as pd
import numpy as np
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
# Group by event and compute per-event quantities
events_df = valid_jets_df.groupby('event_id').agg(
n_jets_valid=('jet_pt', 'count'),
ht_gev=('jet_pt', 'sum'),
lead_jet_e_gev=('jet_e_gev', lambda x: x.iloc[0] if x.iloc[0] is not None else np.nan)
).reset_index()
# Sort events by event_id
events_df = events_df.sort_values('event_id').reset_index(drop=True)
# Save events.csv
events_df.to_csv('events.csv', index=False)
# Compute summary statistics
n_events = len(events_df)
mean_ht_gev = events_df['ht_gev'].mean()
mean_lead_jet_e_gev = events_df['lead_jet_e_gev'].mean()
max_lead_jet_e_gev = events_df['lead_jet_e_gev'].max()
frac_events_ht_gt_200 = (events_df['ht_gev'] > 200).mean()
# Save result.json
result = {
'n_events': int(n_events),
'mean_ht_gev': float(mean_ht_gev),
'mean_lead_jet_e_gev': float(mean_lead_jet_e_gev),
'max_lead_jet_e_gev': float(max_lead_jet_e_gev),
'frac_events_ht_gt_200': float(frac_events_ht_gt_200)
}
with open('result.json', 'w') as f:
f.write(json.dumps(result))
# Save solution.py
with open('solution.py', 'w') as f:
f.write('''import pandas as pd
import numpy as np
import json
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
# Group by event and compute per-event quantities
events_df = valid_jets_df.groupby('event_id').agg(
n_jets_valid=('jet_pt', 'count'),
ht_gev=('jet_pt', 'sum'),
lead_jet_e_gev=('jet_e_gev', lambda x: x.iloc[0] if x.iloc[0] is not None else np.nan)
).reset_index()
# Sort events by event_id
events_df = events_df.sort_values('event_id').reset_index(drop=True)
# Save events.csv
events_df.to_csv('events.csv', index=False)
# Compute summary statistics
n_events = len(events_df)
mean_ht_gev = events_df['ht_gev'].mean()
mean_lead_jet_e_gev = events_df['lead_jet_e_gev'].mean()
max_lead_jet_e_gev = events_df['lead_jet_e_gev'].max()
frac_events_ht_gt_200 = (events_df['ht_gev'] > 200).mean()
# Save result.json
result = {
'n_events': int(n_events),
'mean_ht_gev': float(mean_ht_gev),
'mean_lead_jet_e_gev': float(mean_lead_jet_e_gev),
'max_lead_jet_e_gev': float(max_lead_jet_e_gev),
'frac_events_ht_gt_200': float(frac_events_ht_gt_200)
}
with open('result.json', 'w') as f:
f.write(json.dumps(result))
''')
```
wrote 3080 chars to solution.py
STDERR:
/Users/gariyuu/Projects/agenthep/runs/20260906T175112-t3-unit-mismatch-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py:16: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T175112-t3-unit-mismatch-self_debug-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
runpy.run_path(SCRIPT, run_name="__main__")
File "<frozen runpy>", line 291, in run_path
File "<frozen runpy>", line 98, in _run_module_code
File "<frozen runpy>", line 88, in _run_code
File "/Users/gariyuu/Projects/agenthep/runs/20260906T175112-t3-unit-mismatch-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 47, in <module>
f.write(json.dumps(result))
^^^^
NameError: name 'json' is not defined
exit code: 1
files created: events.csv, result.json```python
import pandas as pd
import numpy as np
import json
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
# Group by event and compute per-event quantities
events_df = valid_jets_df.groupby('event_id').agg(
n_jets_valid=('jet_pt', 'count'),
ht_gev=('jet_pt', 'sum'),
lead_jet_e_gev=('jet_e_gev', lambda x: x.iloc[0] if len(x) > 0 else np.nan)
).reset_index()
# Sort events by event_id
events_df = events_df.sort_values('event_id').reset_index(drop=True)
# Save events.csv
events_df.to_csv('events.csv', index=False)
# Compute summary statistics
n_events = len(events_df)
mean_ht_gev = events_df['ht_gev'].mean()
mean_lead_jet_e_gev = events_df['lead_jet_e_gev'].mean()
max_lead_jet_e_gev = events_df['lead_jet_e_gev'].max()
frac_events_ht_gt_200 = (events_df['ht_gev'] > 200).mean()
# Save result.json
result = {
'n_events': int(n_events),
'mean_ht_gev': float(mean_ht_gev),
'mean_lead_jet_e_gev': float(mean_lead_jet_e_gev),
'max_lead_jet_e_gev': float(max_lead_jet_e_gev),
'frac_events_ht_gt_200': float(frac_events_ht_gt_200)
}
with open('result.json', 'w') as f:
json.dump(result, f)
# Save solution.py
with open('solution.py', 'w') as f:
f.write('''import pandas as pd
import numpy as np
import json
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
# Group by event and compute per-event quantities
events_df = valid_jets_df.groupby('event_id').agg(
n_jets_valid=('jet_pt', 'count'),
ht_gev=('jet_pt', 'sum'),
lead_jet_e_gev=('jet_e_gev', lambda x: x.iloc[0] if len(x) > 0 else np.nan)
).reset_index()
# Sort events by event_id
events_df = events_df.sort_values('event_id').reset_index(drop=True)
# Save events.csv
events_df.to_csv('events.csv', index=False)
# Compute summary statistics
n_events = len(events_df)
mean_ht_gev = events_df['ht_gev'].mean()
mean_lead_jet_e_gev = events_df['lead_jet_e_gev'].mean()
max_lead_jet_e_gev = events_df['lead_jet_e_gev'].max()
frac_events_ht_gt_200 = (events_df['ht_gev'] > 200).mean()
# Save result.json
result = {
'n_events': int(n_events),
'mean_ht_gev': float(mean_ht_gev),
'mean_lead_jet_e_gev': float(mean_lead_jet_e_gev),
'max_lead_jet_e_gev': float(max_lead_jet_e_gev),
'frac_events_ht_gt_200': float(frac_events_ht_gt_200)
}
with open('result.json', 'w') as f:
json.dump(result, f)
''')
```
wrote 3056 chars to solution.py
STDERR: /Users/gariyuu/Projects/agenthep/runs/20260906T175112-t3-unit-mismatch-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0 exit code: 0 files modified: events.csv, result.json, solution.py
Final answer
```python
import pandas as pd
import numpy as np
import json
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
# Group by event and compute per-event quantities
events_df = valid_jets_df.groupby('event_id').agg(
n_jets_valid=('jet_pt', 'count'),
ht_gev=('jet_pt', 'sum'),
lead_jet_e_gev=('jet_e_gev', lambda x: x.iloc[0] if len(x) > 0 else np.nan)
).reset_index()
# Sort events by event_id
events_df = events_df.sort_values('event_id').reset_index(drop=True)
# Save events.csv
events_df.to_csv('events.csv', index=False)
# Compute summary statistics
n_events = len(events_df)
mean_ht_gev = events_df['ht_gev'].mean()
mean_lead_jet_e_gev = events_df['lead_jet_e_gev'].mean()
max_lead_jet_e_gev = events_df['lead_jet_e_gev'].max()
frac_events_ht_gt_200 = (events_df['ht_gev'] > 200).mean()
# Save result.json
result = {
'n_events': int(n_events),
'mean_ht_gev': float(mean_ht_gev),
'mean_lead_jet_e_gev': float(mean_lead_jet_e_gev),
'max_lead_jet_e_gev': float(max_lead_jet_e_gev),
'frac_events_ht_gt_200': float(frac_events_ht_gt_200)
}
with open('result.json', 'w') as f:
json.dump(result, f)
# Save solution.py
with open('solution.py', 'w') as f:
f.write('''import pandas as pd
import numpy as np
import json
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev']
Grader verdict
| Check | Result | Category | Weight | Detail | Observed | Expected |
|---|---|---|---|---|---|---|
| events_existscritical | ✓ pass | artifact | 1 | ok | ||
| result_exists | ✓ pass | artifact | 1 | ok | ||
| solution_exists | ✓ pass | artifact | 1 | ok | ||
| columns | ✓ pass | numeric | 1 | all present | ["event_id","n_jets_valid","ht_gev","lead_jet_e_gev"] | |
| rows | ✗ fail | numeric | 1 | 19996 rows vs expected 20000 | 19996 | 20000 |
| htcritical | ✗ fail | numeric | 3 | 4/20000 values outside tolerance (max |diff| = 0) | {"n_mismatch":4,"max_abs_diff":0} | {"atol":0.001,"rtol":0} |
| lead_ecritical | ✗ fail | numeric | 3 | 13088/20000 values outside tolerance (max |diff| = 1760) | {"n_mismatch":13088,"max_abs_diff":1759.7549999999999} | {"atol":0,"rtol":0.000001} |
| n_valid | ✗ fail | numeric | 1 | 4/20000 values outside tolerance (max |diff| = 0) | {"n_mismatch":4,"max_abs_diff":0} | {"atol":0,"rtol":0} |
| mean_ht | ✗ fail | numeric | 2 | observed 147.39074940988198 vs expected 147.36127126000002 (atol=0.0, rtol=1e-05) | 147.39074940988198 | 147.36127126000002 |
| mean_lead_ecritical | ✗ fail | numeric | 2 | observed 120.42329730946189 vs expected 170.5915307 (atol=0.0, rtol=1e-05) | 120.42329730946189 | 170.5915307 |
| max_lead_e | ✗ fail | numeric | 1 | observed 1821.093 vs expected 2021.512 (atol=0.0, rtol=1e-06) | 1821.093 | 2021.512 |
| frac_ht | ✗ fail | numeric | 1 | observed 0.20249049809961991 vs expected 0.20245 (atol=1e-06, rtol=0.0) | 0.20249049809961991 | 0.20245 |
| reruns | ✓ pass | reproducibility | 1 | reproduced exactly | {"ok":true,"artifacts_reproduced":true,"output_matches":true,"exit_code":0} | |
| not_hardcoded | ✓ pass | compliance | 1 | no expected values hard-coded | [] |
Reproducibility rerun
✓ pass
✓ pass
✓ exact
✓ pass
no randomness used
reproduced exactly
Artifacts
result.json
{"n_events": 19996, "mean_ht_gev": 147.39074940988198, "mean_lead_jet_e_gev": 120.42329730946189, "max_lead_jet_e_gev": 1821.093, "frac_events_ht_gt_200": 0.20249049809961991}solution.py
import pandas as pd
import numpy as np
import json
import os
# Read the data
data_path = 'data/synth_jets.csv'
jets_df = pd.read_csv(data_path)
# Remove duplicates, keeping the first occurrence
jets_df = jets_df.drop_duplicates(subset=['event_id', 'jet_index'], keep='first')
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Convert jet_e_mev to GeV
valid_jets_df['jet_e_gev'] = valid_jets_df['jet_e_mev'] / 1000.0
# Group by event and compute per-event quantities
events_df = valid_jets_df.groupby('event_id').agg(
n_jets_valid=('jet_pt', 'count'),
ht_gev=('jet_pt', 'sum'),
lead_jet_e_gev=('jet_e_gev', lambda x: x.iloc[0] if len(x) > 0 else np.nan)
).reset_index()
# Sort events by event_id
events_df = events_df.sort_values('event_id').reset_index(drop=True)
# Save events.csv
events_df.to_csv('events.csv', index=False)
# Compute summary statistics
n_events = len(events_df)
mean_ht_gev = events_df['ht_gev'].mean()
mean_lead_jet_e_gev = events_df['lead_jet_e_gev'].mean()
max_lead_jet_e_gev = events_df['lead_jet_e_gev'].max()
frac_events_ht_gt_200 = (events_df['ht_gev'] > 200).mean()
# Save result.json
result = {
'n_events': int(n_events),
'mean_ht_gev': float(mean_ht_gev),
'mean_lead_jet_e_gev': float(mean_lead_jet_e_gev),
'max_lead_jet_e_gev': float(max_lead_jet_e_gev),
'frac_events_ht_gt_200': float(frac_events_ht_gt_200)
}
with open('result.json', 'w') as f:
json.dump(result, f)
Run metadata
{
"run_id": "20260906T175112-t3-unit-mismatch-self_debug-gariyuu-qwen3-8b-r0",
"benchmark_version": "1.0.0",
"harness_version": "0.1.0",
"git_sha": "unknown",
"provider": {
"provider": "openai_compat",
"model": "Yuu no Sekai",
"temperature": 0,
"max_tokens": 2500,
"context_tokens": 8192,
"config": {
"base_url": "https://api.gariyuuu.com/v1",
"extra_body": {
"reasoning": {
"enabled": false
}
},
"context_tokens": 8192
},
"captured_at": "2026-09-06T17:51:12.100987+00:00",
"preset": "gariyuu-qwen3-8b",
"family": "qwen3-8b",
"display": "Qwen3-8B (gariyuu gateway)",
"is_mock": false
},
"agent": {
"name": "self_debug",
"max_steps": 25,
"max_debug_rounds": 3
},
"environment": {
"isolation": "seatbelt",
"platform": "macOS-15.1-arm64-arm-64bit",
"python": "3.11.15",
"limits": {
"wall_s": 180,
"cpu_s": 150,
"mem_mb": 2048,
"max_file_mb": 200,
"max_output_chars": 20000
}
},
"started_at": "2026-09-06T17:51:12.066243+00:00",
"finished_at": "2026-09-06T17:51:45.644974+00:00"
}