Inspect the CMS dimuon file
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Task given to the agent
The working directory contains `data/cms_dimuon_2011.csv`, a public CMS 2011 dimuon dataset (see README.md for the data card). Inspect the file and write `result.json` with exactly these keys: - `n_events`: number of rows (int) - `n_columns`: number of columns (int) - `columns`: list of column names in file order - `n_runs`: number of distinct values of `Run` (int) - `n_events_both_global`: number of events where both `type1` and `type2` equal "G" (int) - `m_min`, `m_max`, `m_mean`: minimum, maximum and mean of the dimuon mass column `M` in GeV (floats, full precision) - `frac_opposite_charge`: fraction of events with opposite-sign muon charges, i.e. Q1*Q2 < 0 (float) Save the complete analysis as `solution.py`. Running `python solution.py` from a clean copy of this directory must regenerate `result.json`.
Trajectory
```python
import pandas as pd
import numpy as np
# Read the CSV file
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)
# Compute the required values
n_events = len(df)
n_columns = df.shape[1]
columns = df.columns.tolist()
n_runs = df['Run'].nunique()
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]
# Calculate mass statistics
m_values = df['M']
m_min = m_values.min()
m_max = m_values.max()
m_mean = m_values.mean()
# Calculate fraction of opposite charge events
frac_opposite_charge = (df['Q1'] * df['Q2'] < 0).mean()
# Save results to JSON
result = {
"n_events": int(n_events),
"n_columns": int(n_columns),
"columns": columns,
"n_runs": int(n_runs),
"n_events_both_global": int(n_events_both_global),
"m_min": float(m_min),
"m_max": float(m_max),
"m_mean": float(m_mean),
"frac_opposite_charge": float(frac_opposite_charge)
}
import json
with open('result.json', 'w') as f:
json.dump(result, f)
# Save the complete analysis as solution.py
with open('solution.py', 'w') as f:
f.write('''import pandas as pd
import numpy as np
import json
# Read the CSV file
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)
# Compute the required values
n_events = len(df)
n_columns = df.shape[1]
columns = df.columns.tolist()
n_runs = df['Run'].nunique()
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]
# Calculate mass statistics
m_values = df['M']
m_min = m_values.min()
m_max = m_values.max()
m_mean = m_values.mean()
# Calculate fraction of opposite charge events
frac_opposite_charge = (df['Q1'] * df['Q2'] < 0).mean()
# Save results to JSON
result = {
"n_events": int(n_events),
"n_columns": int(n_columns),
"columns": columns,
"n_runs": int(n_runs),
"n_events_both_global": int(n_events_both_global),
"m_min": float(m_min),
"m_max": float(m_max),
"m_mean": float(m_mean),
"frac_opposite_charge": float(frac_opposite_charge)
}
with open('result.json', 'w') as f:
json.dump(result, f)
''')
# Submit the solution
print("Solution submitted with the following assumptions:")
print("- The data file is correctly formatted and contains no missing values.")
print("- The column names are as specified in the README.md.")
print("- The calculations are performed using the correct formulas.")
```
wrote 2363 chars to solution.py
STDOUT: Solution submitted with the following assumptions: - The data file is correctly formatted and contains no missing values. - The column names are as specified in the README.md. - The calculations are performed using the correct formulas. exit code: 0 files created: result.json files modified: solution.py
Final answer
```python
import pandas as pd
import numpy as np
# Read the CSV file
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)
# Compute the required values
n_events = len(df)
n_columns = df.shape[1]
columns = df.columns.tolist()
n_runs = df['Run'].nunique()
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]
# Calculate mass statistics
m_values = df['M']
m_min = m_values.min()
m_max = m_values.max()
m_mean = m_values.mean()
# Calculate fraction of opposite charge events
frac_opposite_charge = (df['Q1'] * df['Q2'] < 0).mean()
# Save results to JSON
result = {
"n_events": int(n_events),
"n_columns": int(n_columns),
"columns": columns,
"n_runs": int(n_runs),
"n_events_both_global": int(n_events_both_global),
"m_min": float(m_min),
"m_max": float(m_max),
"m_mean": float(m_mean),
"frac_opposite_charge": float(frac_opposite_charge)
}
import json
with open('result.json', 'w') as f:
json.dump(result, f)
# Save the complete analysis as solution.py
with open('solution.py', 'w') as f:
f.write('''import pandas as pd
import numpy as np
import json
# Read the CSV file
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)
# Compute the required values
n_events = len(df)
n_columns = df.shape[1]
columns = df.columns.tolist()
n_runs = df['Run'].nunique()
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]
# Calculate mass statistics
m_values = df['M']
m_min = m_values.min()
m_max = m_values.max()
m_mean = m_values.mean()
# Calculate fraction of opposite charge events
frac_opposite_charge = (df['Q1'] * df['Q2'] < 0).mean()
# Save results to JSON
result = {
"n_events": int(n_events),
"n_columns": int(n_columns),
"columns": columns,
"n_runs": int(n_runs),
"n_events_both_global": int(n_events_both_global),
"m_min": float(m_min),
"m_max": float(m_max),
"m_mean": float(m_mean),
"frac_opposite_charge": float(frac_opposite_
Grader verdict
| Check | Result | Category | Weight | Detail | Observed | Expected |
|---|---|---|---|---|---|---|
| result_existscritical | ✓ pass | artifact | 1 | ok | ||
| solution_exists | ✓ pass | artifact | 1 | ok | ||
| n_eventscritical | ✓ pass | numeric | 2 | match | 100000 | 100000 |
| n_columns | ✓ pass | numeric | 1 | match | 21 | 21 |
| columns | ✓ pass | numeric | 1 | match | ["Run","Event","type1","E1","px1","py1","pz1","pt1"] | ["Run","Event","type1","E1","px1","py1","pz1","pt1"] |
| n_runs | ✓ pass | numeric | 1 | match | 15 | 15 |
| n_both_global | ✓ pass | numeric | 1 | match | 93474 | 93474 |
| m_min | ✓ pass | numeric | 1 | match | 0.3002 | 0.3002 |
| m_max | ✓ pass | numeric | 1 | match | 299.202 | 299.202 |
| m_meancritical | ✓ pass | numeric | 2 | match | 17.691054685 | 17.691054685 |
| frac_os | ✓ pass | numeric | 1 | match | 1 | 1 |
| 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": 100000, "n_columns": 21, "columns": ["Run", "Event", "type1", "E1", "px1", "py1", "pz1", "pt1", "eta1", "phi1", "Q1", "type2", "E2", "px2", "py2", "pz2", "pt2", "eta2", "phi2", "Q2", "M"], "n_runs": 15, "n_events_both_global": 93474, "m_min": 0.3002, "m_max": 299.202, "m_mean": 17.691054685, "frac_opposite_charge": 1.0}solution.py
import pandas as pd
import numpy as np
import json
# Read the CSV file
file_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(file_path)
# Compute the required values
n_events = len(df)
n_columns = df.shape[1]
columns = df.columns.tolist()
n_runs = df['Run'].nunique()
n_events_both_global = df[(df['type1'] == 'G') & (df['type2'] == 'G')].shape[0]
# Calculate mass statistics
m_values = df['M']
m_min = m_values.min()
m_max = m_values.max()
m_mean = m_values.mean()
# Calculate fraction of opposite charge events
frac_opposite_charge = (df['Q1'] * df['Q2'] < 0).mean()
# Save results to JSON
result = {
"n_events": int(n_events),
"n_columns": int(n_columns),
"columns": columns,
"n_runs": int(n_runs),
"n_events_both_global": int(n_events_both_global),
"m_min": float(m_min),
"m_max": float(m_max),
"m_mean": float(m_mean),
"frac_opposite_charge": float(frac_opposite_charge)
}
with open('result.json', 'w') as f:
json.dump(result, f)
Run metadata
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"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": "2026-09-06T17:48:51.255588+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": "2026-09-06T17:48:51.196050+00:00",
"finished_at": "2026-09-06T17:49:03.072811+00:00"
}