Per-event HT and leading-jet energy with a MeV column
0.48
1
2,186
$0.00068
22 s
0
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')
# Convert jet_e_mev to GeV
jets_df['jet_e_gev'] = jets_df['jet_e_mev'] / 1000.0
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Group by event and compute HT (sum of jet_pt in GeV)
ht_per_event = valid_jets_df.groupby('event_id')['jet_pt'].sum().div(1000.0).reset_index()
ht_per_event.columns = ['event_id', 'ht_gev']
# Find leading jet (jet_index == 0) for each event
leading_jet_df = valid_jets_df[valid_jets_df['jet_index'] == 0]
# Extract energy of leading jet
leading_jet_e_gev = leading_jet_df.groupby('event_id')['jet_e_gev'].first().reset_index()
leading_jet_e_gev.columns = ['event_id', 'lead_jet_e_gev']
# Count number of valid jets per event
n_jets_valid = valid_jets_df.groupby('event_id').size().reset_index()
n_jets_valid.columns = ['event_id', 'n_jets_valid']
# Merge all event-level information
events_df = pd.merge(ht_per_event, leading_jet_e_gev, on='event_id', how='left')
events_df = pd.merge(events_df, n_jets_valid, on='event_id', how='left')
# Sort 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).sum() / n_events
# 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:
import json
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 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')
# Convert jet_e_mev to GeV
jets_df['jet_e_gev'] = jets_df['jet_e_mev'] / 1000.0
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Group by event and compute HT (sum of jet_pt in GeV)
ht_per_event = valid_jets_df.groupby('event_id')['jet_pt'].sum().div(1000.0).reset_index()
ht_per_event.columns = ['event_id', 'ht_gev']
# Find leading jet (jet_index == 0) for each event
leading_jet_df = valid_jets_df[valid_jets_df['jet_index'] == 0]
# Extract energy of leading jet
leading_jet_e_gev = leading_jet_df.groupby('event_id')['jet_e_gev'].first().reset_index()
leading_jet_e_gev.columns = ['event_id', 'lead_jet_e_gev']
# Count number of valid jets per event
n_jets_valid = valid_jets_df.groupby('event_id').size().reset_index()
n_jets_valid.columns = ['event_id', 'n_jets_valid']
# Merge all event-level information
events_df = pd.merge(ht_per_event, leading_jet_e_gev, on='event_id', how='left')
events_df = pd.merge(events_df, n_jets_valid, on='event_id', how='left')
# Sort 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).sum() / n_events
# 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:
import json
json.dump(result, f)
''')
```
wrote 4202 chars to solution.py
exit code: 0 files created: events.csv, result.json files modified: solution.py
Final answer
```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')
# Convert jet_e_mev to GeV
jets_df['jet_e_gev'] = jets_df['jet_e_mev'] / 1000.0
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Group by event and compute HT (sum of jet_pt in GeV)
ht_per_event = valid_jets_df.groupby('event_id')['jet_pt'].sum().div(1000.0).reset_index()
ht_per_event.columns = ['event_id', 'ht_gev']
# Find leading jet (jet_index == 0) for each event
leading_jet_df = valid_jets_df[valid_jets_df['jet_index'] == 0]
# Extract energy of leading jet
leading_jet_e_gev = leading_jet_df.groupby('event_id')['jet_e_gev'].first().reset_index()
leading_jet_e_gev.columns = ['event_id', 'lead_jet_e_gev']
# Count number of valid jets per event
n_jets_valid = valid_jets_df.groupby('event_id').size().reset_index()
n_jets_valid.columns = ['event_id', 'n_jets_valid']
# Merge all event-level information
events_df = pd.merge(ht_per_event, leading_jet_e_gev, on='event_id', how='left')
events_df = pd.merge(events_df, n_jets_valid, on='event_id', how='left')
# Sort 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).sum() / n_events
# 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
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","ht_gev","lead_jet_e_gev","n_jets_valid"] | |
| rows | ✗ fail | numeric | 1 | 19996 rows vs expected 20000 | 19996 | 20000 |
| htcritical | ✗ fail | numeric | 3 | 20000/20000 values outside tolerance (max |diff| = 1006) | {"n_mismatch":20000,"max_abs_diff":1005.926067} | {"atol":0.001,"rtol":0} |
| lead_ecritical | ✗ fail | numeric | 3 | 445/20000 values outside tolerance (max |diff| = 0) | {"n_mismatch":445,"max_abs_diff":0} | {"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 0.14739074940988198 vs expected 147.36127126000002 (atol=0.0, rtol=1e-05) | 0.14739074940988198 | 147.36127126000002 |
| mean_lead_ecritical | ✗ fail | numeric | 2 | observed 170.55501370493477 vs expected 170.5915307 (atol=0.0, rtol=1e-05) | 170.55501370493477 | 170.5915307 |
| max_lead_e | ✓ pass | numeric | 1 | match | 2021.512 | 2021.512 |
| frac_ht | ✗ fail | numeric | 1 | observed 0.0 vs expected 0.20245 (atol=1e-06, rtol=0.0) | 0 | 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": 0.14739074940988198, "mean_lead_jet_e_gev": 170.55501370493477, "max_lead_jet_e_gev": 2021.512, "frac_events_ht_gt_200": 0.0}solution.py
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')
# Convert jet_e_mev to GeV
jets_df['jet_e_gev'] = jets_df['jet_e_mev'] / 1000.0
# Filter out jets with missing jet_pt (NaN)
valid_jets_df = jets_df[jets_df['jet_pt'].notna()]
# Group by event and compute HT (sum of jet_pt in GeV)
ht_per_event = valid_jets_df.groupby('event_id')['jet_pt'].sum().div(1000.0).reset_index()
ht_per_event.columns = ['event_id', 'ht_gev']
# Find leading jet (jet_index == 0) for each event
leading_jet_df = valid_jets_df[valid_jets_df['jet_index'] == 0]
# Extract energy of leading jet
leading_jet_e_gev = leading_jet_df.groupby('event_id')['jet_e_gev'].first().reset_index()
leading_jet_e_gev.columns = ['event_id', 'lead_jet_e_gev']
# Count number of valid jets per event
n_jets_valid = valid_jets_df.groupby('event_id').size().reset_index()
n_jets_valid.columns = ['event_id', 'n_jets_valid']
# Merge all event-level information
events_df = pd.merge(ht_per_event, leading_jet_e_gev, on='event_id', how='left')
events_df = pd.merge(events_df, n_jets_valid, on='event_id', how='left')
# Sort 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).sum() / n_events
# 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:
import json
json.dump(result, f)
Run metadata
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